Is OpenAI's Reinforcement Fine-Tuning (RFT) Worth It?

· Andrew Jesson

Summary

  • We benchmarked OpenAI’s supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) workflows on three tasks:

    • Data Extraction: RFT improved performance with limited data, though SFT on a larger dataset achieved better results at a still much lower cost.

    • Agentic Coding: RFT improved performance where SFT failed.
    • Customer Service: SFT outperformed RFT. RFT actually degraded performance.

  • RFT costs 100-700x more than SFT on equivalent datasets. SFT on much larger datasets often still costs only a small fraction of a tiny RFT job.

  • We demonstrate how to implement systematic SFT and RFT workflows programmatically using 10.3KTensorZero.

OpenAI shipped reinforcement fine-tuning (RFT) for o4-mini earlier this year, and at $100 per hour of training time, we had to ask: is this actually worth it?

The pitch sounds compelling: train your model to get better at your specific task using reinforcement learning. But with supervised fine-tuning (SFT) costing literally pennies in comparison, we wanted to stress test whether RFT delivers enough performance gains to justify the 100-700x cost premium.

So we ran the experiment. We took three real-world tasks — data extraction, agentic coding, and customer service (agentic tool use) — and compared RFT against both base o4-mini and supervised fine-tuned GPT-4.1 mini.

The results were… complicated. And expensive. Here’s what we found.

Evaluation

We picked three tasks that cover diverse production use cases for LLMs:

  1. Data Extraction: CoNLL++ NER
  2. Agentic Coding: terminal-bench
  3. Customer Service: τ-bench retail

For each task, we ran the same experiment:

Code to reproduce our results is available in our LLMGym.

Results

1. Data Extraction (CoNLL++ NER)

Task: Extract named entities from the CoNLL++ dataset, identifying persons, locations, organizations, and miscellaneous entities.

Success Criteria: Correctly identify and classify all named entities (e.g. people, organizations) in unstructured text. Success is focused more on understanding the expected format and edge cases rather than actual named entity recognition.

Data Extraction pass^k chart

Training Cost: RFT on 10 training examples cost $65.07 ($50 training, $15.07 grading). SFT on the same data cost $0.09 (723x difference). SFT on all available training examples cost $0.41 (159x difference). Estimated RFT cost for the full dataset: $134.35 (328x increase). [details in appendix]

Key Finding: RFT improved performance on data extraction with just 10 training examples. However, SFT on a larger dataset achieved better results at 159x lower optimization cost, 11x cheaper inference, and 3x faster response times.

CoNLL++ NER Evaluation Details
Example Inference
{
"messages": [
{
"role": "system",
"content": "You are an assistant that is performing a named entity recognition task.\nYour job is to extract entities from a given text.\n\nThe entities you are extracting are:\n- people\n- organizations\n- locations\n- miscellaneous other entities\n\nPlease return the entities in the following JSON format:\n\n{\n \"person\": [\"person1\", \"person2\", ...],\n \"organization\": [\"organization1\", \"organization2\", ...],\n \"location\": [\"location1\", \"location2\", ...],\n \"miscellaneous\": [\"miscellaneous1\", \"miscellaneous2\", ...]\n}"
},
{
"role": "user",
"content": "7. Linda Kisabaka ( Germany ) 52.41"
},
{
"role": "assistant",
"content": "{\n \"person\": [\"Linda Kisabaka\"],\n \"organization\": [],\n \"location\": [\"Germany\"],\n \"miscellaneous\": []\n}"
}
],
}

Fine-tuning data:

10 training inferences using o4-mini (127 inferences in full training set)

2 validation inferences using o4-mini (31 inferences in full validation set)

500 test tasks with 5 repetitions

Grader Developer Template
You are an expert grader of model-generated named entity recognition (NER) outputs.
In the evaluation data section below, you will be given a message history and a reference answer from a successful completion of the NER task.
Your job is to rate the accuracy of the model answer compared to the reference answer.
## Task Context
- **Task**: Extract and categorize named entities from text
- **Input**: Text containing various named entities
- **Output**: JSON format with entities categorized into person, organization, location, and miscellaneous
- **Goal**: Correctly identify all relevant entities with proper categorization
## Entity Categories
- **Person**: Names of individuals (e.g., "John Smith", "Dr. Sarah Johnson")
- **Organization**: Companies, institutions, government bodies, etc. (e.g., "Microsoft", "Harvard University", "Department of Defense")
- **Location**: Geographic locations including countries, cities, landmarks, etc. (e.g., "New York", "Mount Everest", "Pacific Ocean")
- **Miscellaneous**: Other named entities that don't fit the above categories (e.g., events, products, dates, awards)
## Scoring Rubric
Evaluate and assign a single float score (0.0-1.0) based on the criteria below:
### Key Factors
- **Completeness**: Are all relevant entities identified?
- **Accuracy**: Are identified entities actually present in the text?
- **Categorization**: Are entities placed in the correct categories?
- **Format**: Is the JSON structure valid and properly formatted?
### What Makes an Answer Optimal?
- **Perfect entity recall**: All ground truth entities are identified
- **Perfect entity precision**: No false positive entities
- **Correct categorization**: All entities placed in appropriate categories
- **Exact text matching**: Entities appear verbatim as they do in the source text (e.g., "LEICESTERSHIRE" and "Leicestershire" are not equivalent)
- **Valid JSON format**: Proper structure and formatting
### Scores
| Score | Description |
| -------- | ------------------------------------------------------------------------------------------------------------------------------- |
| **1.0** | Perfect match: all entities correctly identified and categorized with no false positives |
| **0.75** | Mostly correct with minor omissions (missing 1-2 entities) or minor miscategorizations |
| **0.5** | Partially correct but notable issues: missing several key entities, significant miscategorizations, or moderate false positives |
| **0.25** | Significantly inaccurate: many missed entities, frequent miscategorizations, or numerous false positives |
| **0.0** | Completely incorrect: fails to identify most entities, severe categorization errors, or invalid JSON format |
### Invalid Answer Criteria (score = 0.0)
An answer is invalid if it meets at least one of the following conditions:
- Invalid JSON format or structure
- Fails to identify the majority of entities present in the text
- Severe systematic categorization errors
Grader User Template
## Evaluation Data
### Message History
{{ item.messages }}
### Reference Answer
{{ item.reference_text }}
### Model Answer
{{ sample.output_json }}
Grader Response Format Schema
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "ExtractEntitiesOutputSchema",
"type": "object",
"properties": {
"person": {
"type": "array",
"items": {
"type": "string"
}
},
"organization": {
"type": "array",
"items": {
"type": "string"
}
},
"location": {
"type": "array",
"items": {
"type": "string"
}
},
"miscellaneous": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": ["person", "organization", "location", "miscellaneous"],
"additionalProperties": false
}

More Details: Agent Config and Replication Commands

2. Agentic Coding (terminal-bench)

Task: Complete complex computational tasks in a terminal environment including compiling code repositories, training machine learning models, configuring servers, and performing data analysis through multi-step command sequences.

Success: Agent successfully executes all required terminal commands and produces the correct output as verified by automated test scripts in isolated Docker environments.

Agentic Coding pass^k chart

Training Cost: RFT on 2 training conversations (17 inferences) cost $168.57 ($131.67 training, $36.90 grading). SFT on the same data cost $0.70 (241x difference). SFT on all available conversations cost $3.29 (51x difference). Estimated RFT cost for the full dataset: $400.95 (122x increase). [details in appendix]

Key Finding: RFT significantly improved performance on agentic coding tasks with just 2 training conversations (17 inferences). SFT showed performance degradation relative to baseline on both equivalent and full training datasets.

terminal-bench Evaluation Details
Example Conversation
{
"messages": [
{
"role": "system",
"content": "You are an AI assistant tasked with solving command-line tasks in a Linux environment.\nYou will be given a task description and the output from previously executed commands.\nYour goal is to solve the task by making structured tool calls.\n\n## Tools Available\n\nYou have access to the following tools:\n\n- `think(thought)`: Analyze critical decisions and ambiguous states. Keep focused and concise.\n\n- `plan(next_steps)`: Define your approach with specific commands and expected outcomes.\n\n- `execute_command(command, duration)`: Execute shell commands.\n - `command`: EXACT keystrokes followed by a NEWLINE (\\n), used verbatim. (e.g. \"ls -la\\n\")\n - `duration`: Seconds to wait (default 1.0). Start short - you can always poll.\n\n- `submit_solution()`: Submit when task is complete.\n\nCall one tool at a time. Do not run multiple commands simultaneously.\n\n## Efficiency Guidelines\n\n1. **Verify Before Testing**: Check changes with `cat` or `grep` before running tests\n2. **Single Execution**: Run tests once unless debugging specific failures\n3. **Concise Reasoning**: Keep think() for critical decisions only\n4. **Command Chaining**: Use \"&&\" for related commands (e.g., \"cd /app && ls -la\\n\")\n5. **Smart Durations**: Start short (0.1s) - you can always poll. Never exceed 60s.\n6. **Explore Efficiently**: Use \"grep -n\\n\" with patterns or \"find\" to locate content quickly\n\n## Special Keys\n\nUse tmux-style sequences for terminal control:\n- \"C-c\" for Ctrl+C (interrupt)\n- \"C-d\" for Ctrl+D (EOF)\n- \"C-z\" for Ctrl+Z (suspend)\n\n## Common Patterns\n\n### File Editing Without Interactive Editors:\n```bash\n# Small changes\nsed -i 's/old_pattern/new_pattern/g' filename\n\n# Complete rewrites\ncat > filename << 'EOF'\nnew content here\nEOF\n\n# Append to file\ncat >> filename << 'EOF'\nadditional content\nEOF\n\n# Check before and after\ndiff -u file.backup file # see what changed\n```\n\n### Debugging Failed Tests:\n1. Check error output carefully\n2. Test after each fix attempt before trying alternatives\n3. Verify file modifications: \"cat filename\\n | grep 'expected_content\\n'\"\n4. Check syntax: `python3 -m py_compile filename.py\\n`\n5. Add debug prints if needed\n\n### Error Recovery:\n- **Command not found**: Check PATH or use full path\n- **Permission denied**: Check ownership with \"ls -la\\n\"\n- **File not found**: Verify path and working directory\n- **Network issues**: Check /etc/hosts, /etc/resolv.conf, test connectivity\n- **Test failures**: Add debug output, check logs\n- **Timeout**: Increase duration or break into steps\n- **File changes not taking effect**: May need to wait or restart services\n\n## Task Approach\n\n1. **Understand**: Analyze task requirements and current state\n2. **Plan**: Identify specific steps and commands needed\n3. **Execute**: Run commands with appropriate durations\n4. **Verify**: Check outputs and test fixes before proceeding\n5. **Submit**: Complete task only after confirming success\n\n## Response Format\n\nUse structured tool calls only. Progress systematically through analysis, planning, execution, and completion.\n\nRemember:\n- Include \"\\n\" at end of all commands\n- Verify changes before testing\n- Use minimum necessary tool calls\n- Chain related commands with \"&&\" or pipes\n- Poll long operations with `execute_command(\"\", duration)` if needed"
},
{
"role": "user",
"content": "\n# Task:\nWrite me a single file in /app/main.rs which is a polyglot, that is, I can run `rustc main.rs && ./main N` or `g++ -x c++ main.rs -o cmain && ./cmain N`. Both of these should print to stdout the kth fibonacci number. f(0) = 1, f(1) = 1, f(2) = 2.\n\n\n"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_3pm8GBgnV9rZFhydG3o8DTvd",
"type": "function",
"function": {
"name": "think",
"arguments": "{\"thought\":\"We need to craft a single file /app/main.rs that compiles both as Rust and as C++ and computes kth Fibonacci. This is tricky; perhaps there are hints in project or previous examples. Let's inspect /app.\"}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_3pm8GBgnV9rZFhydG3o8DTvd"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_t3sIfj5CTf7uaDcPL4cdBVlI",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"ls -la /app\\n\",\"purpose\":\"Explore repository contents to understand constraints and existing files\"},{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"purpose\":\"Check if a main.rs exists and its content\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_t3sIfj5CTf7uaDcPL4cdBVlI"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_JigENfBeZfvppPUZhKWk87md",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"ls -la /app\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "Current Terminal Screen:\nroot@3cc7f9157144:/app# ls -la /app\ntotal 8\ndrwxr-xr-x 2 root root 4096 May 28 03:31 .\ndrwxr-xr-x 1 root root 4096 Aug 26 15:27 ..\nroot@3cc7f9157144:/app#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
"tool_call_id": "call_JigENfBeZfvppPUZhKWk87md"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_9GIwzgx3x2r7lv9NLMguxGah",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# sed -n '1,200p' /app/main.rs\nsed: can't read /app/main.rs: No such file or directory\nroot@3cc7f9157144:/app#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
"tool_call_id": "call_9GIwzgx3x2r7lv9NLMguxGah"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_v5fiIbces0VAmDUIwxQ9kvqc",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Add File: /app/main.rs\\n+/*/*\\n+*/\\n+#include <iostream>\\n+#include <cstdlib>\\n+using namespace std;\\n+int main(int argc, char** argv) {\\n+ unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n+ unsigned long long a = 1, b = 1;\\n+ for (unsigned long long i = 0; i < n; ++i) {\\n+ unsigned long long t = b;\\n+ b = a + b;\\n+ a = t;\\n+ }\\n+ cout << a << \\\"\\\\n\\\";\\n+ return 0;\\n+}\\n+#if 0\\n+// */\\n+use std::env;\\n+fn main() {\\n+ let args: Vec<String> = env::args().collect();\\n+ let n: u64 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n+ let mut a: u128 = 1;\\n+ let mut b: u128 = 1;\\n+ for _ in 0..n {\\n+ let t = b;\\n+ b = a + b;\\n+ a = t;\\n+ }\\n+ println!(\\\"{}\\\", a);\\n+}\\n+/*\\n+#endif\\n+// */\\n+\\n*** End Patch\\nPATCH\\n\",\"purpose\":\"Create the polyglot file with carefully crafted nested comments and preprocessor directives so both Rust and C++ can compile and run. Then we will test both toolchains.\"},{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"purpose\":\"Verify the file content\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_v5fiIbces0VAmDUIwxQ9kvqc"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_G1eoGxnYegVlxr803F4HzvWf",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"applypatch << 'PATCH'\\n*** Begin Patch\\n*** Add File: /app/main.rs\\n+/*/*\\n+*/\\n+#include <iostream>\\n+#include <cstdlib>\\n+using namespace std;\\n+int main(int argc, char** argv) {\\n+ unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n+ unsigned long long a = 1, b = 1;\\n+ for (unsigned long long i = 0; i < n; ++i) {\\n+ unsigned long long t = b;\\n+ b = a + b;\\n+ a = t;\\n+ }\\n+ cout << a << \\\"\\\\n\\\";\\n+ return 0;\\n+}\\n+#if 0\\n+// */\\n+use std::env;\\n+fn main() {\\n+ let args: Vec<String> = env::args().collect();\\n+ let n: u64 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n+ let mut a: u128 = 1;\\n+ let mut b: u128 = 1;\\n+ for _ in 0..n {\\n+ let t = b;\\n+ b = a + b;\\n+ a = t;\\n+ }\\n+ println!(\\\"{}\\\", a);\\n+}\\n+/*\\n+#endif\\n+// */\\n+\\n*** End Patch\\nPATCH\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# applypatch << 'PATCH'\n> *** Begin Patch\n> *** Add File: /app/main.rs\n> +/*/*\n> +*/\n> +#include <iostream>\n> +#include <cstdlib>\n> +using namespace std;\n> +int main(int argc, char** argv) {\n> + unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\n> + unsigned long long a = 1, b = 1;\n> + for (unsigned long long i = 0; i < n; ++i) {\n> + unsigned long long t = b;\n> + b = a + b;\n> + a = t;\n> + }\n> + cout << a << \"\\n\";\n> + return 0;\n> +}\n> +#if 0\n> +// */\n> +use std::env;\n> +fn main() {\n> + let args: Vec<String> = env::args().collect();\n> + let n: u64 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\n> + let mut a: u128 = 1;\n> + let mut b: u128 = 1;\n> + for _ in 0..n {\n> + let t = b;\n> + b = a + b;\n> + a = t;\n> + }\n> + println!(\"{}\", a);\n> +}\n> +/*\n> +#endif\n> +// */\n> +\n> *** End Patch\n> PATCH\nbash: applypatch: command not found\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_G1eoGxnYegVlxr803F4HzvWf"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_Vg3JjD6IfmIPiP2TzYDw9D6a",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"cat > /app/main.rs << 'EOF'\\n/*/*/\\n#include <iostream>\\n#include <cstdlib>\\nusing namespace std;\\nint main(int argc, char** argv) {\\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n unsigned long long a = 1, b = 1;\\n for (unsigned long long i = 0; i < n; ++i) {\\n unsigned long long t = b;\\n b = a + b;\\n a = t;\\n }\\n cout << a << \\\"\\\\n\\\";\\n return 0;\\n}\\n#if 0\\n// */\\nuse std::env;\\nfn main() {\\n let args: Vec<String> = env::args().collect();\\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n let mut a: u128 = 1;\\n let mut b: u128 = 1;\\n for _ in 0..n {\\n let t = b;\\n b = a + b;\\n a = t;\\n }\\n println!(\\\"{}\\\", a);\\n}\\n/*/*/\\n#endif\\n// */\\nEOF\\n\",\"purpose\":\"Create the Rust/C++ polyglot file with a structure that opens a Rust-only comment, compiles C++ code, then uses #if 0 to skip Rust code for C++, closes Rust comment to enable Rust code, reopens a Rust-only comment to hide #endif from Rust, and finally ends.\"},{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"purpose\":\"Verify the written content for correctness\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_Vg3JjD6IfmIPiP2TzYDw9D6a"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_DmSXlKIBHTv9MClKH8tya8Tz",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"cat > /app/main.rs << 'EOF'\\n/*/*/\\n#include <iostream>\\n#include <cstdlib>\\nusing namespace std;\\nint main(int argc, char** argv) {\\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n unsigned long long a = 1, b = 1;\\n for (unsigned long long i = 0; i < n; ++i) {\\n unsigned long long t = b;\\n b = a + b;\\n a = t;\\n }\\n cout << a << \\\"\\\\n\\\";\\n return 0;\\n}\\n#if 0\\n// */\\nuse std::env;\\nfn main() {\\n let args: Vec<String> = env::args().collect();\\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n let mut a: u128 = 1;\\n let mut b: u128 = 1;\\n for _ in 0..n {\\n let t = b;\\n b = a + b;\\n a = t;\\n }\\n println!(\\\"{}\\\", a);\\n}\\n/*/*/\\n#endif\\n// */\\nEOF\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# cat > /app/main.rs << 'EOF'\n> /*/*/\n> #include <iostream>\n> #include <cstdlib>\n> using namespace std;\n> int main(int argc, char** argv) {\n> unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\n> unsigned long long a = 1, b = 1;\n> for (unsigned long long i = 0; i < n; ++i) {\n> unsigned long long t = b;\n> b = a + b;\n> a = t;\n> }\n> cout << a << \"\\n\";\n> return 0;\n> }\n> #if 0\n> // */\n> use std::env;\n> fn main() {\n> let args: Vec<String> = env::args().collect();\n> let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\n> let mut a: u128 = 1;\n> let mut b: u128 = 1;\n> for _ in 0..n {\n> let t = b;\n> b = a + b;\n> a = t;\n> }\n> println!(\"{}\", a);\n> }\n> /*/*/\n> #endif\n> // */\n> EOF\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_DmSXlKIBHTv9MClKH8tya8Tz"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_uTPgFXINE3TtCQ0tx1pghwjm",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# sed -n '1,200p' /app/main.rs\n/*/*/\n#include <iostream>\n#include <cstdlib>\nusing namespace std;\nint main(int argc, char** argv) {\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\n unsigned long long a = 1, b = 1;\n for (unsigned long long i = 0; i < n; ++i) {\n unsigned long long t = b;\n b = a + b;\n a = t;\n }\n cout << a << \"\\n\";\n return 0;\n}\n#if 0\n// */\nuse std::env;\nfn main() {\n let args: Vec<String> = env::args().collect();\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\n let mut a: u128 = 1;\n let mut b: u128 = 1;\n for _ in 0..n {\n let t = b;\n b = a + b;\n a = t;\n }\n println!(\"{}\", a);\n}\n/*/*/\n#endif\n// */\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_uTPgFXINE3TtCQ0tx1pghwjm"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_2MhgJsn29McWC6uaRbO3G5ei",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"cd /app && rustc main.rs && ./main 10\\n\",\"purpose\":\"Test whether the current file actually compiles and runs as Rust. Expect failures based on analysis.\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_2MhgJsn29McWC6uaRbO3G5ei"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_6OM4q9m6ajfizLjcOtfyeJgU",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"cd /app && rustc main.rs && ./main 10\\n\",\"duration\":3}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# cd /app && rustc main.rs && ./main 10\nerror[E0758]: unterminated block comment\n --> main.rs:1:1\n |\n1 | / /*/*/\n2 | | #include <iostream>\n3 | | #include <cstdlib>\n4 | | using namespace std;\n... |\n32 | | #endif\n33 | | // */\n | |______^\n\nerror: aborting due to previous error\n\nFor more information about this error, try `rustc --explain E0758`.\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_6OM4q9m6ajfizLjcOtfyeJgU"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_3OqMzVsrNuHZ8KCk2Fj9QON2",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"cat > /app/main.rs << 'EOF'\\n/* /*\\n*/\\n#include <iostream>\\n#include <cstdlib>\\nusing namespace std;\\nint main(int argc, char** argv) {\\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n unsigned long long a = 1, b = 1;\\n for (unsigned long long i = 0; i < n; ++i) {\\n unsigned long long t = b;\\n b = a + b;\\n a = t;\\n }\\n cout << a << \\\"\\\\n\\\";\\n return 0;\\n}\\n#if 0\\n// */\\nuse std::env;\\nfn main() {\\n let args: Vec<String> = env::args().collect();\\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n let mut a: u128 = 1;\\n let mut b: u128 = 1;\\n for _ in 0..n {\\n let t = b;\\n b = a + b;\\n a = t;\\n }\\n println!(\\\"{}\\\", a);\\n}\\n/* /*\\n*/\\n#endif\\n// */\\nEOF\\n\",\"purpose\":\"Rewrite main.rs with a robust nested comment polyglot structure that hides the appropriate sections from each compiler and reveals the correct parts.\"},{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"purpose\":\"Verify the file contents\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_3OqMzVsrNuHZ8KCk2Fj9QON2"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_r0BiyeLb2FBsX4LKYkBW0Odf",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"cat > /app/main.rs << 'EOF'\\n/* /*\\n*/\\n#include <iostream>\\n#include <cstdlib>\\nusing namespace std;\\nint main(int argc, char** argv) {\\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\\n unsigned long long a = 1, b = 1;\\n for (unsigned long long i = 0; i < n; ++i) {\\n unsigned long long t = b;\\n b = a + b;\\n a = t;\\n }\\n cout << a << \\\"\\\\n\\\";\\n return 0;\\n}\\n#if 0\\n// */\\nuse std::env;\\nfn main() {\\n let args: Vec<String> = env::args().collect();\\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\\n let mut a: u128 = 1;\\n let mut b: u128 = 1;\\n for _ in 0..n {\\n let t = b;\\n b = a + b;\\n a = t;\\n }\\n println!(\\\"{}\\\", a);\\n}\\n/* /*\\n*/\\n#endif\\n// */\\nEOF\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# cat > /app/main.rs << 'EOF'\n> /* /*\n> */\n> #include <iostream>\n> #include <cstdlib>\n> using namespace std;\n> int main(int argc, char** argv) {\n> unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\n> unsigned long long a = 1, b = 1;\n> for (unsigned long long i = 0; i < n; ++i) {\n> unsigned long long t = b;\n> b = a + b;\n> a = t;\n> }\n> cout << a << \"\\n\";\n> return 0;\n> }\n> #if 0\n> // */\n> use std::env;\n> fn main() {\n> let args: Vec<String> = env::args().collect();\n> let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\n> let mut a: u128 = 1;\n> let mut b: u128 = 1;\n> for _ in 0..n {\n> let t = b;\n> b = a + b;\n> a = t;\n> }\n> println!(\"{}\", a);\n> }\n> /* /*\n> */\n> #endif\n> // */\n> EOF\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_r0BiyeLb2FBsX4LKYkBW0Odf"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_rbCxuCccYHvbTjipan5YsaPk",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"sed -n '1,200p' /app/main.rs\\n\",\"duration\":0.1}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# sed -n '1,200p' /app/main.rs\n/* /*\n*/\n#include <iostream>\n#include <cstdlib>\nusing namespace std;\nint main(int argc, char** argv) {\n unsigned long long n = (argc > 1) ? strtoull(argv[1], nullptr, 10) : 0ULL;\n unsigned long long a = 1, b = 1;\n for (unsigned long long i = 0; i < n; ++i) {\n unsigned long long t = b;\n b = a + b;\n a = t;\n }\n cout << a << \"\\n\";\n return 0;\n}\n#if 0\n// */\nuse std::env;\nfn main() {\n let args: Vec<String> = env::args().collect();\n let n: u128 = args.get(1).and_then(|s| s.parse().ok()).unwrap_or(0);\n let mut a: u128 = 1;\n let mut b: u128 = 1;\n for _ in 0..n {\n let t = b;\n b = a + b;\n a = t;\n }\n println!(\"{}\", a);\n}\n/* /*\n*/\n#endif\n// */\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_rbCxuCccYHvbTjipan5YsaPk"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_KIZuE9R9mc3JKSd4ZKvPfGe6",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"cd /app && rustc main.rs && ./main 10\\n\",\"purpose\":\"Test compiling as Rust again after rewrite.\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_KIZuE9R9mc3JKSd4ZKvPfGe6"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_iXhtO2qd6BiU40SDTYahskLX",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"cd /app && rustc main.rs && ./main 10\\n\",\"duration\":3}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# cd /app && rustc main.rs && ./main 10\n89\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_iXhtO2qd6BiU40SDTYahskLX"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_lAGZMdopvy9RUaXt5iIe0tLq",
"type": "function",
"function": {
"name": "plan",
"arguments": "{\"next_steps\":[{\"command\":\"cd /app && g++ -x c++ main.rs -o cmain && ./cmain 10\\n\",\"purpose\":\"Test compiling and running as C++ to ensure polyglot requirement is met.\"}]}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_lAGZMdopvy9RUaXt5iIe0tLq"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_NuBDqtyPzvtSGJ6XNL6gRx17",
"type": "function",
"function": {
"name": "execute_command",
"arguments": "{\"command\":\"cd /app && g++ -x c++ main.rs -o cmain && ./cmain 10\\n\",\"duration\":3}"
}
}
]
},
{
"role": "tool",
"content": "New Terminal Output:\n\nroot@3cc7f9157144:/app# cd /app && g++ -x c++ main.rs -o cmain && ./cmain 10\n89\nroot@3cc7f9157144:/app#\n",
"tool_call_id": "call_NuBDqtyPzvtSGJ6XNL6gRx17"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_hMW06ZOOvJB1RAtfYqreUQLT",
"type": "function",
"function": {
"name": "think",
"arguments": "{\"thought\":\"Both compilers produce correct output for f(10)=89 as per definition starting f(0)=1. We should submit the solution. The file path is correct.\"}"
}
}
]
},
{
"role": "tool",
"content": "",
"tool_call_id": "call_hMW06ZOOvJB1RAtfYqreUQLT"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_pX4yMYkAlljz5y5ro0TgCy68",
"type": "function",
"function": {
"name": "submit_solution",
"arguments": "{}"
}
}
]
}
],
}

Fine-tuning data:

2 training conversations using o4-mini comprising 17 inferences (16 conversations comprising 237 inferences in full training set)

1 validation conversation using o4-mini comprising 19 inferences (3 conversations comprising 48 inferences in full validation set)

39 test tasks with 5 repetitions

Grader Developer Template
You are an expert grader of model-generated command-line task solutions in Linux environments.
In the evaluation data section below, you will be given a message history and a reference solution from a successful completion of a terminal task.
Your job is to rate whether the model's solution is better or worse than the reference solution given the provided message history.
## Task Context
- **Task**: Solve command-line tasks in a Linux terminal environment
- **Input**: Task description and terminal output from previously executed commands
- **Tools**: think (reasoning), plan (approach definition), execute_command (shell execution), submit_solution (task completion)
- **Output**: Tool calls in JSON format to systematically solve the terminal task
- **Goal**: Efficiently complete the task using appropriate commands and verification steps
## Message History Structure (Chronological Order, Oldest First)
- **'role': 'user'**: Contains the task description and any terminal output/feedback
- **'role': 'assistant'**: Previously generated tool calls (if any exist)
- **'role': 'user'**: Command execution results (stdout, stderr, terminal state)
- The final **'role': 'user'** message contains the most recent terminal output relevant to the tool call you are evaluating
## Tool Call Types and Evaluation Criteria
### Think Tool
- **Purpose**: Analyze critical decisions, debug issues, and reason about ambiguous states
- **Good Usage**: Focused analysis of errors, decision points about approach, understanding complex output
- **Poor Usage**: Excessive verbosity, using for trivial decisions, redundant reasoning, stream-of-consciousness
### Plan Tool
- **Purpose**: Define structured approach with specific commands and expected outcomes
- **Good Usage**: Clear step-by-step breakdown, specific commands with purposes, realistic expectations
- **Poor Usage**: Vague steps, missing command details, unrealistic or incorrect expected outcomes
### Execute Command Tool
- **Purpose**: Run shell commands in the terminal environment
- **Good Usage**:
- Correct command syntax with proper newline termination (\n)
- Appropriate duration settings (start short, increase if needed)
- Efficient command chaining with && or pipes
- Proper use of non-interactive editing (sed, cat with heredocs)
- Verification before destructive operations
- **Poor Usage**:
- Missing newline termination
- Excessive durations (>60s)
- Using interactive editors (vim, nano)
- Running tests repeatedly without changes
- Not verifying file changes before testing
- Incorrect special key sequences (should use C-c, C-d, C-z format)
### Submit Solution Tool
- **Purpose**: Mark the task as complete after successful completion
- **Good Usage**: Called only after verifying task completion, all requirements met
- **Poor Usage**: Premature submission, task incomplete, tests failing, requirements unmet
## Efficiency and Best Practice Guidelines
### Command Execution Patterns
- **Optimal**: Chain related commands, use appropriate tools (grep, find, sed), verify changes before testing
- **Suboptimal**: Running commands one at a time unnecessarily, using cat to read entire large files, testing without verification
### File Editing Approaches
- **Optimal**: Use sed for replacements, cat with heredocs for rewrites, diff to verify changes
- **Suboptimal**: Attempting to use interactive editors, making changes without backup, not verifying edits
### Debugging Strategies
- **Optimal**: Check error output carefully, test incrementally, add targeted debug output
- **Suboptimal**: Repeatedly running same failing command, not reading error messages, excessive debug output
### Duration Management
- **Optimal**: Start with short durations (0.1-1.0s), increase only when needed, poll long operations
- **Suboptimal**: Using excessive durations initially, waiting unnecessarily, not polling when appropriate
## Scoring Rubric
Evaluate and assign a single float score (0.0-1.0) based on the criteria below:
### Definitions
- **Optimal Solution**: Efficiently solves the task with minimal commands, proper verification, correct syntax, and appropriate tool usage following best practices.
- **Suboptimal Solution**: Solves the task but with inefficiencies, unnecessary steps, poor command choices, or violations of best practices.
### Key Factors
- **Correctness**: Commands execute properly and achieve intended results
- **Efficiency**: Minimal necessary commands, appropriate chaining, smart duration choices
- **Best Practices**: Proper verification, non-interactive editing, error handling
- **Tool Usage**: Appropriate tool selection and parameter usage
- **Task Completion**: Successfully achieves all task requirements
- **Comparison to Reference**: Evaluated relative to the successful reference solution provided
### Scores
| Score | Description |
|-------|-------------|
| **1.0** | Solution is optimal: efficiently completes task with best practices, minimal commands, proper verification, and is equal or superior to the reference. |
| **0.75** | Solution is clearly better than the reference with more efficient approach, better error handling, or cleaner implementation. |
| **0.5** | Solution equals the reference, both complete the task acceptably but have room for improvement. |
| **0.25** | Solution is valid but distinctly worse than the reference (inefficient, poor practices, excessive commands). |
| **0.0** | Solution is invalid due to critical errors, task incompletion, or fundamental approach problems. |
### Invalid Solution Criteria (score = 0.0)
A solution is invalid if it meets at least one of the following conditions:
- Commands have syntax errors preventing execution
- Task remains incomplete after submit_solution call
- Critical requirements are not met
- Destructive operations without proper verification
- Infinite loops or hung processes without recovery
- Submits solution when tests are failing
### Common Efficiency Issues (maximum score = 0.5)
Even if task is completed, solutions with these issues cannot score above 0.5:
- Using interactive editors (vim, nano, emacs) instead of sed/cat
- Running same test multiple times without making changes
- Not verifying file modifications before testing
- Using excessive durations (>30s) without justification
- Not chaining related commands when appropriate
- Reading entire large files with cat instead of using grep/head/tail
Grader User Template
## Evaluation Data
### Message History
{{ item.messages }}
### Reference Answer
{% if item.reference_tools is defined and item.reference_tools %}
**Tools**
{{ item.reference_tools }}
{% endif %}
{% if item.reference_text is defined and item.reference_text %}
**Text**
{{ item.reference_text }}
{% endif %}
### Model Answer
**Tools**
{{ sample.output_tools }}
**Text**
{{ sample.output_text }}

More Details: Agent Config and Replication Commands

3. Customer Service (τ-bench retail)

Task: Handle multi-turn customer service conversations with tool use for order management including cancellations, modifications, returns, exchanges, and address updates while following domain-specific policies and authentication requirements.

Success: Agent completes the user’s request with correct database changes (measured by state comparison) and communicates all required information.

Customer Service pass^k chart

Training Cost: RFT on 2 training conversations (23 inferences) cost $201.85 ($151.67 training, $50.18 grading). SFT on the same data cost $1.80 (112x difference). SFT on all available conversations cost $29.27 (7x difference). Estimated RFT cost for the full dataset: $10,591.23 (362x increase). [details in appendix]

Key Finding: RFT showed performance degradation relative to baseline on customer service tasks with 2 training conversations (23 inferences), whereas SFT showed improvements both on the equivalent and full training dataset.

τ-bench Retail Evaluation Details
Example Conversation
{
"messages": [
{
"role": "system",
"content": "# Retail agent policy\n\nAs a retail agent, you can help users cancel or modify pending orders, return or exchange delivered orders, modify their default user address, or provide information about their own profile, orders, and related products.\n\n- At the beginning of the conversation, you have to authenticate the user identity by locating their user id via email, or via name + zip code. This has to be done even when the user already provides the user id.\n\n- Once the user has been authenticated, you can provide the user with information about order, product, profile information, e.g. help the user look up order id.\n\n- You can only help one user per conversation (but you can handle multiple requests from the same user), and must deny any requests for tasks related to any other user.\n\n- Before taking consequential actions that update the database (cancel, modify, return, exchange), you have to list the action detail and obtain explicit user confirmation (yes) to proceed.\n\n- You should not make up any information or knowledge or procedures not provided from the user or the tools, or give subjective recommendations or comments.\n\n- You should at most make one tool call at a time, and if you take a tool call, you should not respond to the user at the same time. If you respond to the user, you should not make a tool call.\n\n- You should transfer the user to a human agent if and only if the request cannot be handled within the scope of your actions.\n\n## Domain basic\n\n- All times in the database are EST and 24 hour based. For example \"02:30:00\" means 2:30 AM EST.\n\n- Each user has a profile of its email, default address, user id, and payment methods. Each payment method is either a gift card, a paypal account, or a credit card.\n\n- Our retail store has 50 types of products. For each type of product, there are variant items of different options. For example, for a 't shirt' product, there could be an item with option 'color blue size M', and another item with option 'color red size L'.\n\n- Each product has an unique product id, and each item has an unique item id. They have no relations and should not be confused.\n\n- Each order can be in status 'pending', 'processed', 'delivered', or 'cancelled'. Generally, you can only take action on pending or delivered orders.\n\n- Exchange or modify order tools can only be called once. Be sure that all items to be changed are collected into a list before making the tool call!!!\n\n## Cancel pending order\n\n- An order can only be cancelled if its status is 'pending', and you should check its status before taking the action.\n\n- The user needs to confirm the order id and the reason (either 'no longer needed' or 'ordered by mistake') for cancellation.\n\n- After user confirmation, the order status will be changed to 'cancelled', and the total will be refunded via the original payment method immediately if it is gift card, otherwise in 5 to 7 business days.\n\n## Modify pending order\n\n- An order can only be modified if its status is 'pending', and you should check its status before taking the action.\n\n- For a pending order, you can take actions to modify its shipping address, payment method, or product item options, but nothing else.\n\n### Modify payment\n\n- The user can only choose a single payment method different from the original payment method.\n\n- If the user wants the modify the payment method to gift card, it must have enough balance to cover the total amount.\n\n- After user confirmation, the order status will be kept 'pending'. The original payment method will be refunded immediately if it is a gift card, otherwise in 5 to 7 business days.\n\n### Modify items\n\n- This action can only be called once, and will change the order status to 'pending (items modifed)', and the agent will not be able to modify or cancel the order anymore. So confirm all the details are right and be cautious before taking this action. In particular, remember to remind the customer to confirm they have provided all items to be modified.\n\n- For a pending order, each item can be modified to an available new item of the same product but of different product option. There cannot be any change of product types, e.g. modify shirt to shoe.\n\n- The user must provide a payment method to pay or receive refund of the price difference. If the user provides a gift card, it must have enough balance to cover the price difference.\n\n## Return delivered order\n\n- An order can only be returned if its status is 'delivered', and you should check its status before taking the action.\n\n- The user needs to confirm the order id, the list of items to be returned, and a payment method to receive the refund.\n\n- The refund must either go to the original payment method, or an existing gift card.\n\n- After user confirmation, the order status will be changed to 'return requested', and the user will receive an email regarding how to return items.\n\n## Exchange delivered order\n\n- An order can only be exchanged if its status is 'delivered', and you should check its status before taking the action. In particular, remember to remind the customer to confirm they have provided all items to be exchanged.\n\n- For a delivered order, each item can be exchanged to an available new item of the same product but of different product option. There cannot be any change of product types, e.g. modify shirt to shoe.\n\n- The user must provide a payment method to pay or receive refund of the price difference. If the user provides a gift card, it must have enough balance to cover the price difference.\n\n- After user confirmation, the order status will be changed to 'exchange requested', and the user will receive an email regarding how to return items. There is no need to place a new order."
},
{
"role": "user",
"content": "Hi! I need to update the address for my order #W5166363. Can you help with that?"
},
{
"role": "assistant",
"content": "I can help with that. First, I need to verify your account.\n\n- What’s the email on your account? If you don’t remember, please provide your first and last name and ZIP code instead.\n- Also, please share the new shipping address you want on order #W5166363:\n - Address line 1\n - Address line 2 (if any)\n - City\n - State/Province\n - Country\n - ZIP/Postal code\n\nOnce verified, I’ll check the order status and, if it’s still pending, I’ll confirm the change details with you before updating the address."
},
{
"role": "user",
"content": "Sure! My email is lei.li8350@example.com. The new address is 604 Pine Lane, Suite 907, Phoenix, AZ, USA, 85033."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_ikvIZDAGu1nvqy4GoVgW8ZQL",
"type": "function",
"function": {
"name": "find_user_id_by_email",
"arguments": "{\"email\":\"lei.li8350@example.com\"}"
}
}
]
},
{
"role": "tool",
"content": "lei_li_6575",
"tool_call_id": "call_ikvIZDAGu1nvqy4GoVgW8ZQL"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_UW3yJj6fmwAvlr3mnQ9hWLy7",
"type": "function",
"function": {
"name": "get_order_details",
"arguments": "{\"order_id\":\"#W5166363\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"order_id\": \"#W5166363\", \"user_id\": \"lei_li_6575\", \"address\": {\"address1\": \"905 Highland Drive\", \"address2\": \"Suite 807\", \"city\": \"San Francisco\", \"country\": \"USA\", \"state\": \"CA\", \"zip\": \"94132\"}, \"items\": [{\"name\": \"Laptop\", \"product_id\": \"4760268021\", \"item_id\": \"3334537816\", \"price\": 2749.56, \"options\": {\"screen size\": \"17-inch\", \"processor\": \"i5\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"space grey\"}}], \"fulfillments\": [], \"status\": \"pending\", \"payment_history\": [{\"transaction_type\": \"payment\", \"amount\": 2749.56, \"payment_method_id\": \"paypal_5914760\"}]}",
"tool_call_id": "call_UW3yJj6fmwAvlr3mnQ9hWLy7"
},
{
"role": "assistant",
"content": "Thanks, I’ve verified your account (lei_li_6575) and checked order #W5166363. It’s still pending, so we can update the shipping address.\n\n- Current shipping address on the order:\n - 905 Highland Drive, Suite 807, San Francisco, CA, 94132, USA\n- New shipping address you provided:\n - 604 Pine Lane, Suite 907, Phoenix, AZ, 85033, USA\n\nPlease confirm: Do you want me to update the shipping address on order #W5166363 to the new address above? Reply “Yes” to proceed or “No” to cancel. \n\nNote: This will only update the address for this order. If you also want to change your default address on your profile, let me know."
},
{
"role": "user",
"content": "Yes, please update the shipping address for order #W5166363 to the new address."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_iLvm3m3gCgAk2PXIfhbkmqpJ",
"type": "function",
"function": {
"name": "modify_pending_order_address",
"arguments": "{\"order_id\":\"#W5166363\",\"address1\":\"604 Pine Lane\",\"address2\":\"Suite 907\",\"city\":\"Phoenix\",\"state\":\"AZ\",\"country\":\"USA\",\"zip\":\"85033\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"order_id\": \"#W5166363\", \"user_id\": \"lei_li_6575\", \"address\": {\"address1\": \"604 Pine Lane\", \"address2\": \"Suite 907\", \"city\": \"Phoenix\", \"state\": \"AZ\", \"country\": \"USA\", \"zip\": \"85033\"}, \"items\": [{\"name\": \"Laptop\", \"product_id\": \"4760268021\", \"item_id\": \"3334537816\", \"price\": 2749.56, \"options\": {\"screen size\": \"17-inch\", \"processor\": \"i5\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"space grey\"}}], \"fulfillments\": [], \"status\": \"pending\", \"payment_history\": [{\"transaction_type\": \"payment\", \"amount\": 2749.56, \"payment_method_id\": \"paypal_5914760\"}]}",
"tool_call_id": "call_iLvm3m3gCgAk2PXIfhbkmqpJ"
},
{
"role": "assistant",
"content": "All set—your shipping address for order #W5166363 has been updated.\n\nNew shipping address:\n- 604 Pine Lane, Suite 907, Phoenix, AZ, 85033, USA\n\nNothing else on the order was changed, and it remains in pending status.\n\nIf you’d also like me to update your default profile address to this new one, just say yes."
},
{
"role": "user",
"content": "Great, thanks! Now, I also need to make some changes to the laptop on that order. Can you help with that?"
},
{
"role": "assistant",
"content": "I can help with that. A quick note: for pending orders, we can only change item options within the same product (e.g., processor, RAM, storage, screen size, color). Once we submit the item change, we won’t be able to modify or cancel the order again, so please include all changes in one go.\n\nCurrent laptop config on #W5166363:\n- Screen: 17-inch\n- Processor: i5\n- RAM: 8GB\n- Storage: 1TB SSD\n- Color: space grey\n\nPlease tell me the exact configuration you want to switch to (list all option changes). If you know the target item ID, share that instead.\n\nAlso, which payment method should we use for any price difference (charge or refund)?\n- Keep your current PayPal on file\n- Or choose another saved method (credit card or gift card; gift card must have enough balance)"
},
{
"role": "user",
"content": "I'd like to change the configuration to a 15-inch screen, i7 processor, and black color. Please use my saved credit card ending in 4466831 for any price difference."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_glg2lDVK4ytYrUVkflFWSXim",
"type": "function",
"function": {
"name": "get_product_details",
"arguments": "{\"product_id\":\"4760268021\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"name\": \"Laptop\", \"product_id\": \"4760268021\", \"variants\": {\"8997785118\": {\"item_id\": \"8997785118\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i7\", \"ram\": \"32GB\", \"storage\": \"256GB SSD\", \"color\": \"space grey\"}, \"available\": false, \"price\": 2674.4}, \"2216662955\": {\"item_id\": \"2216662955\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i5\", \"ram\": \"32GB\", \"storage\": \"256GB SSD\", \"color\": \"space grey\"}, \"available\": true, \"price\": 2520.52}, \"2768401027\": {\"item_id\": \"2768401027\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i7\", \"ram\": \"32GB\", \"storage\": \"256GB SSD\", \"color\": \"silver\"}, \"available\": false, \"price\": 2346.49}, \"1684786391\": {\"item_id\": \"1684786391\", \"options\": {\"screen size\": \"17-inch\", \"processor\": \"i7\", \"ram\": \"32GB\", \"storage\": \"1TB SSD\", \"color\": \"black\"}, \"available\": true, \"price\": 2508.06}, \"3778566150\": {\"item_id\": \"3778566150\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i5\", \"ram\": \"32GB\", \"storage\": \"256GB SSD\", \"color\": \"silver\"}, \"available\": false, \"price\": 2372.97}, \"8193934556\": {\"item_id\": \"8193934556\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i9\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"space grey\"}, \"available\": false, \"price\": 2548.73}, \"2913673670\": {\"item_id\": \"2913673670\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i9\", \"ram\": \"32GB\", \"storage\": \"512GB SSD\", \"color\": \"black\"}, \"available\": true, \"price\": 2701.89}, \"3478699712\": {\"item_id\": \"3478699712\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i5\", \"ram\": \"16GB\", \"storage\": \"512GB SSD\", \"color\": \"space grey\"}, \"available\": false, \"price\": 2291.87}, \"6056040996\": {\"item_id\": \"6056040996\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i5\", \"ram\": \"16GB\", \"storage\": \"512GB SSD\", \"color\": \"space grey\"}, \"available\": true, \"price\": 2609.37}, \"6017636844\": {\"item_id\": \"6017636844\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i7\", \"ram\": \"32GB\", \"storage\": \"1TB SSD\", \"color\": \"space grey\"}, \"available\": true, \"price\": 2292.37}, \"1657832319\": {\"item_id\": \"1657832319\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i7\", \"ram\": \"32GB\", \"storage\": \"512GB SSD\", \"color\": \"black\"}, \"available\": true, \"price\": 2729.32}, \"5052031638\": {\"item_id\": \"5052031638\", \"options\": {\"screen size\": \"13-inch\", \"processor\": \"i5\", \"ram\": \"16GB\", \"storage\": \"1TB SSD\", \"color\": \"silver\"}, \"available\": true, \"price\": 2621.77}, \"3265035808\": {\"item_id\": \"3265035808\", \"options\": {\"screen size\": \"17-inch\", \"processor\": \"i9\", \"ram\": \"8GB\", \"storage\": \"256GB SSD\", \"color\": \"silver\"}, \"available\": true, \"price\": 2530.72}, \"3334537816\": {\"item_id\": \"3334537816\", \"options\": {\"screen size\": \"17-inch\", \"processor\": \"i5\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"space grey\"}, \"available\": false, \"price\": 2749.56}, \"9844888101\": {\"item_id\": \"9844888101\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i7\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"black\"}, \"available\": true, \"price\": 2459.74}, \"4241599783\": {\"item_id\": \"4241599783\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i7\", \"ram\": \"16GB\", \"storage\": \"1TB SSD\", \"color\": \"black\"}, \"available\": false, \"price\": 2324.61}, \"2611676054\": {\"item_id\": \"2611676054\", \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i7\", \"ram\": \"16GB\", \"storage\": \"256GB SSD\", \"color\": \"silver\"}, \"available\": false, \"price\": 2743.08}}}",
"tool_call_id": "call_glg2lDVK4ytYrUVkflFWSXim"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_Qm1Uleo8jSLhIPeH2cz70cM0",
"type": "function",
"function": {
"name": "get_user_details",
"arguments": "{\"user_id\":\"lei_li_6575\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"name\": {\"first_name\": \"Lei\", \"last_name\": \"Li\"}, \"address\": {\"address1\": \"604 Pine Lane\", \"address2\": \"Suite 907\", \"city\": \"Phoenix\", \"country\": \"USA\", \"state\": \"AZ\", \"zip\": \"85033\"}, \"email\": \"lei.li8350@example.com\", \"payment_methods\": {\"credit_card_4466831\": {\"source\": \"credit_card\", \"brand\": \"visa\", \"last_four\": \"2697\", \"id\": \"credit_card_4466831\"}, \"gift_card_8049813\": {\"source\": \"gift_card\", \"balance\": 50, \"id\": \"gift_card_8049813\"}, \"paypal_5914760\": {\"source\": \"paypal\", \"id\": \"paypal_5914760\"}}, \"orders\": [\"#W5166363\", \"#W3414433\", \"#W6289770\", \"#W3189752\"]}",
"tool_call_id": "call_Qm1Uleo8jSLhIPeH2cz70cM0"
},
{
"role": "assistant",
"content": "Thanks. I found an available match for your requested configuration.\n\nProposed new item:\n- 15-inch, i7, 8GB RAM, 1TB SSD, black (item 9844888101)\n- Price: $2,459.74\n\nYour current item:\n- 17-inch, i5, 8GB RAM, 1TB SSD, space grey (item 3334537816)\n- Price: $2,749.56\n\nPrice difference and payment:\n- You’ll receive a refund of $289.82 to your saved Visa ending 2697 (payment method ID: credit_card_4466831).\n\nImportant:\n- Modifying items on a pending order can only be done once and will prevent any further modifications or cancellation of the order.\n- This is the only item on the order. Please confirm there are no other changes.\n\nPlease confirm: Change item 3334537816 to 9844888101 on order #W5166363 and process the $289.82 refund to credit_card_4466831? Reply “Yes” to proceed or “No” to cancel."
},
{
"role": "user",
"content": "Yes, please make the change to item 9844888101 and process the refund to my credit card ending in 4466831."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_FZ1mdipnll2fBRarKcrmZ60Q",
"type": "function",
"function": {
"name": "modify_pending_order_items",
"arguments": "{\"order_id\":\"#W5166363\",\"item_ids\":[\"3334537816\"],\"new_item_ids\":[\"9844888101\"],\"payment_method_id\":\"credit_card_4466831\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"order_id\": \"#W5166363\", \"user_id\": \"lei_li_6575\", \"address\": {\"address1\": \"604 Pine Lane\", \"address2\": \"Suite 907\", \"city\": \"Phoenix\", \"state\": \"AZ\", \"country\": \"USA\", \"zip\": \"85033\"}, \"items\": [{\"name\": \"Laptop\", \"product_id\": \"4760268021\", \"item_id\": \"9844888101\", \"price\": 2459.74, \"options\": {\"screen size\": \"15-inch\", \"processor\": \"i7\", \"ram\": \"8GB\", \"storage\": \"1TB SSD\", \"color\": \"black\"}}], \"fulfillments\": [], \"status\": \"pending (item modified)\", \"payment_history\": [{\"transaction_type\": \"payment\", \"amount\": 2749.56, \"payment_method_id\": \"paypal_5914760\"}, {\"transaction_type\": \"refund\", \"amount\": 289.82000000000016, \"payment_method_id\": \"credit_card_4466831\"}]}",
"tool_call_id": "call_FZ1mdipnll2fBRarKcrmZ60Q"
},
{
"role": "assistant",
"content": "Done. Your order has been updated.\n\nOrder #W5166363\n- Item changed to: 15-inch, i7, 8GB RAM, 1TB SSD, black (item 9844888101)\n- Status: pending (item modified)\n- Refund: $289.82 to Visa ending 2697 (payment method ID: credit_card_4466831). Refunds to credit cards typically post in 5–7 business days.\n\nYour shipping address remains:\n- 604 Pine Lane, Suite 907, Phoenix, AZ 85033, USA\n\nNote: Item changes on pending orders can only be done once, and further modifications or cancellation aren’t available now.\n\nAnything else I can help you with today?"
},
{
"role": "user",
"content": "Yes, I also need to exchange a couple of items on a different order, #W6289770."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_NmZffcFQ7lmUsOoD5ULJpooZ",
"type": "function",
"function": {
"name": "get_order_details",
"arguments": "{\"order_id\":\"#W6289770\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"order_id\": \"#W6289770\", \"user_id\": \"lei_li_6575\", \"address\": {\"address1\": \"604 Pine Lane\", \"address2\": \"Suite 907\", \"city\": \"Phoenix\", \"country\": \"USA\", \"state\": \"AZ\", \"zip\": \"85033\"}, \"items\": [{\"name\": \"Electric Toothbrush\", \"product_id\": \"7352963235\", \"item_id\": \"8098621301\", \"price\": 192.15, \"options\": {\"color\": \"black\", \"speed settings\": \"high\", \"battery type\": \"rechargeable\"}}, {\"name\": \"Grill\", \"product_id\": \"6819683148\", \"item_id\": \"1120917161\", \"price\": 953.39, \"options\": {\"type\": \"electric\", \"size\": \"portable\", \"features\": \"none\"}}, {\"name\": \"Water Bottle\", \"product_id\": \"8310926033\", \"item_id\": \"6974536207\", \"price\": 49.3, \"options\": {\"capacity\": \"750ml\", \"material\": \"plastic\", \"color\": \"blue\"}}, {\"name\": \"Luggage Set\", \"product_id\": \"5426915165\", \"item_id\": \"8964750292\", \"price\": 532.58, \"options\": {\"piece count\": \"2-piece\", \"color\": \"red\", \"material\": \"hardshell\"}}, {\"name\": \"Sunglasses\", \"product_id\": \"7314138884\", \"item_id\": \"4548300368\", \"price\": 287.79, \"options\": {\"frame color\": \"black\", \"lens color\": \"green\", \"lens type\": \"polarized\", \"frame material\": \"plastic\"}}], \"fulfillments\": [{\"tracking_id\": [\"114641927258\"], \"item_ids\": [\"8098621301\", \"1120917161\", \"6974536207\", \"8964750292\", \"4548300368\"]}], \"status\": \"delivered\", \"payment_history\": [{\"transaction_type\": \"payment\", \"amount\": 2015.21, \"payment_method_id\": \"credit_card_4466831\"}]}",
"tool_call_id": "call_NmZffcFQ7lmUsOoD5ULJpooZ"
},
{
"role": "assistant",
"content": "I can help with that. Order #W6289770 is delivered and eligible for exchange. Please include all items you want to exchange in one request (we can only submit one exchange per delivered order), and note each exchange must stay within the same product type (just different options).\n\nHere are the items on #W6289770:\n- Electric Toothbrush — item 8098621301 — black, high speed, rechargeable — $192.15\n- Grill — item 1120917161 — electric, portable, no extra features — $953.39\n- Water Bottle — item 6974536207 — 750ml, plastic, blue — $49.30\n- Luggage Set — item 8964750292 — 2-piece, red, hardshell — $532.58\n- Sunglasses — item 4548300368 — black frame, green polarized lens, plastic frame — $287.79\n\nPlease tell me:\n1) Which item(s) you want to exchange (by name or item ID).\n2) The exact options you want for each (or the target item IDs if you know them).\n3) Which payment method to use for any price difference:\n - Visa ending 2697 (credit_card_4466831)\n - PayPal (paypal_5914760)\n - Gift card (gift_card_8049813; current balance $50)\n\nOnce I have the details, I’ll summarize the exchange and ask for your confirmation to proceed. You’ll receive an email with return instructions after the exchange is submitted."
},
{
"role": "user",
"content": "I'd like to exchange the Grill to a charcoal, medium, with a side burner. For the Sunglasses, exchange them for non-polarized lenses and metal frame. Use my credit card ending in 4466831 for any price difference."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_2LzGRROYEivyGIhxA397pDLb",
"type": "function",
"function": {
"name": "get_product_details",
"arguments": "{\"product_id\":\"6819683148\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"name\": \"Grill\", \"product_id\": \"6819683148\", \"variants\": {\"4404981319\": {\"item_id\": \"4404981319\", \"options\": {\"type\": \"electric\", \"size\": \"large\", \"features\": \"rotisserie\"}, \"available\": true, \"price\": 1031.0}, \"3876764226\": {\"item_id\": \"3876764226\", \"options\": {\"type\": \"electric\", \"size\": \"portable\", \"features\": \"side burner\"}, \"available\": true, \"price\": 981.47}, \"5745575001\": {\"item_id\": \"5745575001\", \"options\": {\"type\": \"electric\", \"size\": \"portable\", \"features\": \"rotisserie\"}, \"available\": true, \"price\": 986.65}, \"5666020311\": {\"item_id\": \"5666020311\", \"options\": {\"type\": \"electric\", \"size\": \"medium\", \"features\": \"side burner\"}, \"available\": false, \"price\": 1058.86}, \"7082455361\": {\"item_id\": \"7082455361\", \"options\": {\"type\": \"charcoal\", \"size\": \"medium\", \"features\": \"rotisserie\"}, \"available\": true, \"price\": 962.69}, \"9724317332\": {\"item_id\": \"9724317332\", \"options\": {\"type\": \"gas\", \"size\": \"portable\", \"features\": \"side burner\"}, \"available\": true, \"price\": 1042.19}, \"7848293342\": {\"item_id\": \"7848293342\", \"options\": {\"type\": \"charcoal\", \"size\": \"medium\", \"features\": \"side burner\"}, \"available\": true, \"price\": 942.71}, \"1120917161\": {\"item_id\": \"1120917161\", \"options\": {\"type\": \"electric\", \"size\": \"portable\", \"features\": \"none\"}, \"available\": false, \"price\": 953.39}, \"5105441284\": {\"item_id\": \"5105441284\", \"options\": {\"type\": \"charcoal\", \"size\": \"portable\", \"features\": \"none\"}, \"available\": true, \"price\": 924.5}, \"5946177616\": {\"item_id\": \"5946177616\", \"options\": {\"type\": \"gas\", \"size\": \"portable\", \"features\": \"none\"}, \"available\": true, \"price\": 1057.24}, \"7717598293\": {\"item_id\": \"7717598293\", \"options\": {\"type\": \"electric\", \"size\": \"medium\", \"features\": \"rotisserie\"}, \"available\": false, \"price\": 985.66}, \"6589665742\": {\"item_id\": \"6589665742\", \"options\": {\"type\": \"gas\", \"size\": \"large\", \"features\": \"rotisserie\"}, \"available\": false, \"price\": 933.17}}}",
"tool_call_id": "call_2LzGRROYEivyGIhxA397pDLb"
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_c7ZYyye5kmS4qN4qwfQIt6M9",
"type": "function",
"function": {
"name": "get_product_details",
"arguments": "{\"product_id\":\"7314138884\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"name\": \"Sunglasses\", \"product_id\": \"7314138884\", \"variants\": {\"2177260429\": {\"item_id\": \"2177260429\", \"options\": {\"frame color\": \"black\", \"lens color\": \"green\", \"lens type\": \"polarized\", \"frame material\": \"metal\"}, \"available\": false, \"price\": 296.47}, \"4548300368\": {\"item_id\": \"4548300368\", \"options\": {\"frame color\": \"black\", \"lens color\": \"green\", \"lens type\": \"polarized\", \"frame material\": \"plastic\"}, \"available\": true, \"price\": 287.79}, \"4358482460\": {\"item_id\": \"4358482460\", \"options\": {\"frame color\": \"black\", \"lens color\": \"brown\", \"lens type\": \"polarized\", \"frame material\": \"plastic\"}, \"available\": true, \"price\": 290.94}, \"2198125883\": {\"item_id\": \"2198125883\", \"options\": {\"frame color\": \"silver\", \"lens color\": \"black\", \"lens type\": \"polarized\", \"frame material\": \"metal\"}, \"available\": true, \"price\": 296.16}, \"9045948550\": {\"item_id\": \"9045948550\", \"options\": {\"frame color\": \"black\", \"lens color\": \"blue\", \"lens type\": \"polarized\", \"frame material\": \"metal\"}, \"available\": false, \"price\": 279.78}, \"4245201809\": {\"item_id\": \"4245201809\", \"options\": {\"frame color\": \"black\", \"lens color\": \"green\", \"lens type\": \"non-polarized\", \"frame material\": \"metal\"}, \"available\": true, \"price\": 281.48}, \"9672174103\": {\"item_id\": \"9672174103\", \"options\": {\"frame color\": \"brown\", \"lens color\": \"brown\", \"lens type\": \"polarized\", \"frame material\": \"plastic\"}, \"available\": true, \"price\": 281.98}, \"4329558751\": {\"item_id\": \"4329558751\", \"options\": {\"frame color\": \"silver\", \"lens color\": \"blue\", \"lens type\": \"non-polarized\", \"frame material\": \"plastic\"}, \"available\": true, \"price\": 297.33}}}",
"tool_call_id": "call_c7ZYyye5kmS4qN4qwfQIt6M9"
},
{
"role": "assistant",
"content": "Thanks. Here’s what I can exchange to match your request. Please review and confirm all details.\n\nGrill\n- Current: item 1120917161 — electric, portable, no features — $953.39\n- New: item 7848293342 — charcoal, medium, side burner — $942.71\n- Difference: refund $10.68\n\nSunglasses\n- Current: item 4548300368 — black frame, green, polarized, plastic — $287.79\n- New: item 4245201809 — black frame, green, non-polarized, metal — $281.48\n- Difference: refund $6.31\n\nTotals\n- Total refund: $16.99 to your Visa (credit_card_4466831)\n\nNotes\n- Exchanges on delivered orders can only be submitted once. This request will include both items above.\n- You’ll receive return instructions by email after submission.\n\nPlease confirm: Exchange items 1120917161 -> 7848293342 and 4548300368 -> 4245201809 on order #W6289770, with the $16.99 refund to credit_card_4466831? If you prefer different colors for the sunglasses, tell me before we proceed."
},
{
"role": "user",
"content": "Yes, please proceed with the exchanges as outlined, and process the refund to my credit card."
},
{
"role": "assistant",
"tool_calls": [
{
"id": "call_7MBAr195QYdEFkCpfIm1m3pv",
"type": "function",
"function": {
"name": "exchange_delivered_order_items",
"arguments": "{\"order_id\":\"#W6289770\",\"item_ids\":[\"1120917161\",\"4548300368\"],\"new_item_ids\":[\"7848293342\",\"4245201809\"],\"payment_method_id\":\"credit_card_4466831\"}"
}
}
]
},
{
"role": "tool",
"content": "{\"order_id\": \"#W6289770\", \"user_id\": \"lei_li_6575\", \"address\": {\"address1\": \"604 Pine Lane\", \"address2\": \"Suite 907\", \"city\": \"Phoenix\", \"country\": \"USA\", \"state\": \"AZ\", \"zip\": \"85033\"}, \"items\": [{\"name\": \"Electric Toothbrush\", \"product_id\": \"7352963235\", \"item_id\": \"8098621301\", \"price\": 192.15, \"options\": {\"color\": \"black\", \"speed settings\": \"high\", \"battery type\": \"rechargeable\"}}, {\"name\": \"Grill\", \"product_id\": \"6819683148\", \"item_id\": \"1120917161\", \"price\": 953.39, \"options\": {\"type\": \"electric\", \"size\": \"portable\", \"features\": \"none\"}}, {\"name\": \"Water Bottle\", \"product_id\": \"8310926033\", \"item_id\": \"6974536207\", \"price\": 49.3, \"options\": {\"capacity\": \"750ml\", \"material\": \"plastic\", \"color\": \"blue\"}}, {\"name\": \"Luggage Set\", \"product_id\": \"5426915165\", \"item_id\": \"8964750292\", \"price\": 532.58, \"options\": {\"piece count\": \"2-piece\", \"color\": \"red\", \"material\": \"hardshell\"}}, {\"name\": \"Sunglasses\", \"product_id\": \"7314138884\", \"item_id\": \"4548300368\", \"price\": 287.79, \"options\": {\"frame color\": \"black\", \"lens color\": \"green\", \"lens type\": \"polarized\", \"frame material\": \"plastic\"}}], \"fulfillments\": [{\"tracking_id\": [\"114641927258\"], \"item_ids\": [\"8098621301\", \"1120917161\", \"6974536207\", \"8964750292\", \"4548300368\"]}], \"status\": \"exchange requested\", \"payment_history\": [{\"transaction_type\": \"payment\", \"amount\": 2015.21, \"payment_method_id\": \"credit_card_4466831\"}], \"exchange_items\": [\"1120917161\", \"4548300368\"], \"exchange_new_items\": [\"4245201809\", \"7848293342\"], \"exchange_payment_method_id\": \"credit_card_4466831\", \"exchange_price_difference\": -16.99}",
"tool_call_id": "call_7MBAr195QYdEFkCpfIm1m3pv"
},
{
"role": "assistant",
"content": "Your exchange request has been submitted.\n\nOrder #W6289770\n- Grill: 1120917161 -> 7848293342 (charcoal, medium, side burner)\n- Sunglasses: 4548300368 -> 4245201809 (black, green, non-polarized, metal)\n- Status: exchange requested\n- Refund total: $16.99 to Visa ending 2697 (credit_card_4466831). Refunds to credit cards typically post in 5–7 business days.\n\nYou’ll receive an email shortly with instructions for returning the original items. Once they’re received, we’ll ship the replacements.\n\nAnything else I can help you with today?"
},
],
}

Fine-tuning data:

2 training conversations using o4-mini comprising 23 inferences (332 conversations comprising 3866 inferences in full training set)

1 validation conversation using o4-mini comprising 15 inferences (82 conversations comprising 921 inferences in full validation set)

115 test tasks with 5 repetitions

Grader Developer Template
You are an expert grader of model-generated retail customer service interactions.
In the evaluation data section below, you will be given a message history and a reference response from a successful completion of a retail service task.
Your job is to rate whether the model's response is better or worse than the reference response given the provided message history.
## Task Context
- **Task**: Handle retail customer service requests including order cancellations, modifications, returns, exchanges, and information retrieval
- **Input**: Customer request and conversation history with tool call results
- **Tools**: Various database and order management tools for authentication, lookups, and order operations
- **Output**: Natural language responses and appropriate tool calls to resolve customer issues
- **Goal**: Efficiently and accurately handle customer requests while following all retail policies
## Message History Structure (Chronological Order, Oldest First)
- **'role': 'user'**: Contains the customer request and any follow-up information
- **'role': 'assistant'**: Previously generated responses and tool calls (if any exist)
- **'role': 'user'**: Tool results (authentication results, order details, operation confirmations)
- The final **'role': 'user'** message contains the most recent information relevant to the response you are evaluating
## Tool Call Types and Evaluation Criteria
### Authentication Tools
#### find_user_id_by_email
- **Purpose**: Authenticate user using their email address
- **Good Usage**: Called at conversation start with correct email format
- **Poor Usage**: Skipped authentication, incorrect email format, or called after already authenticated
#### find_user_id_by_name_zip
- **Purpose**: Authenticate user using name and zip code combination
- **Good Usage**: Used when email not provided, both parameters correctly supplied
- **Poor Usage**: Missing parameters, used when email already provided, or incorrect format
### Information Retrieval Tools
#### get_user_details / get_order_details / get_product_details
- **Purpose**: Retrieve specific information about users, orders, or products
- **Good Usage**: Called with correct ID format (note '#' prefix for order IDs), used to verify status before operations
- **Poor Usage**: Incorrect ID format, unnecessary repeated calls, or not checking status before modifications
#### list_all_product_types
- **Purpose**: Show available product catalog
- **Good Usage**: Called when customer needs product information or browsing options
- **Poor Usage**: Called unnecessarily when not relevant to customer request
### Order Modification Tools (Pending Orders)
#### cancel_pending_order
- **Purpose**: Cancel orders with status 'pending'
- **Good Usage**: Status verified first, valid cancellation reason provided, explicit confirmation obtained
- **Poor Usage**: Attempting on non-pending orders, invalid reason, or no user confirmation
#### modify_pending_order_address / modify_pending_order_payment
- **Purpose**: Update shipping address or payment method for pending orders
- **Good Usage**: Status checked, valid new information provided, user confirmation obtained
- **Poor Usage**: Modifying already-modified orders, invalid payment methods, or insufficient gift card balance
#### modify_pending_order_items
- **Purpose**: Change item options within same product type (SINGLE USE per order)
- **Good Usage**: All items collected before calling, same product types maintained, arrays properly matched
- **Poor Usage**: Called multiple times per order, product type changes, mismatched array lengths, or incomplete item collection
### Order Return/Exchange Tools (Delivered Orders)
#### return_delivered_order_items
- **Purpose**: Process returns for delivered orders
- **Good Usage**: Status verified as 'delivered', all return items collected, valid refund method specified
- **Poor Usage**: Attempting on non-delivered orders, partial item collection, or invalid refund destination
#### exchange_delivered_order_items
- **Purpose**: Exchange items for different options of same product (SINGLE USE per order)
- **Good Usage**: All exchange items collected before calling, product types maintained, payment method valid
- **Poor Usage**: Multiple calls per order, product type changes, incomplete item collection, or array mismatches
### Utility Tools
#### think
- **Purpose**: Complex reasoning and tracking conversation state
- **Good Usage**: Planning multi-step operations, tracking authentication state, reasoning about policies
- **Poor Usage**: Excessive use for simple tasks or using as communication method with customer
#### transfer_to_human_agents
- **Purpose**: Escalate issues beyond agent capabilities
- **Good Usage**: Request genuinely outside policy scope, after attempting available solutions
- **Poor Usage**: Premature escalation without trying available tools or for requests clearly within scope
#### calculate
- **Purpose**: Perform mathematical calculations
- **Good Usage**: Computing price differences, totals, or refund amounts
- **Poor Usage**: Unnecessary calculations or incorrect formula usage
### User Address Management
#### modify_user_address
- **Purpose**: Update user's default address in profile
- **Good Usage**: Complete address information provided, user confirmation obtained
- **Poor Usage**: Incomplete address data or modifying without confirmation
## Core Policy Requirements
### Authentication
- **Must authenticate** user via email OR name + zip code at conversation start
- **Must verify** user identity even if user provides their user ID directly
- **One user per conversation**: Cannot help with requests for other users
### Order Operations
- **Pending Orders**: Can be cancelled or modified (shipping address, payment, items)
- **Delivered Orders**: Can be returned or exchanged
- **Confirmation Required**: Must obtain explicit "yes" from user before any database-modifying action
- **Single Tool Call Rule**: Make one tool call at a time, not multiple in same response
### Information Accuracy
- **No fabrication**: Cannot make up information not provided by tools
- **No subjective advice**: Must avoid personal recommendations or opinions
- **Exact procedures only**: Follow documented policies without improvisation
## Scoring Rubric
Evaluate and assign a single float score (0.0-1.0) based on the criteria below:
### Definitions
- **Optimal Response**: Follows all policies correctly, advances the customer interaction appropriately, uses correct tools with proper parameters, and maintains professional service standards.
- **Suboptimal Response**: Partially helpful but violates minor policies, inefficient tool usage, or unclear communication with customer.
### Key Factors
- **Policy Compliance**: Adherence to authentication, confirmation, and operational policies
- **Tool Usage**: Correct tool selection and parameter usage
- **Customer Communication**: Clear, professional, and accurate responses
- **Process Efficiency**: Logical progression toward resolving the customer request
- **Comparison to Reference**: Evaluated relative to the successful reference response provided
### Scores
| Score | Description |
|-------|-------------|
| **1.0** | Response is optimal: follows all policies perfectly, uses appropriate tools correctly, communicates clearly, and is equal or superior to the reference. |
| **0.75** | Response is clearly better than the reference but has minor imperfections (e.g., slightly verbose, minor inefficiency). |
| **0.5** | Response equals the reference, both are acceptable but have room for improvement. |
| **0.25** | Response is valid but distinctly worse than the reference (policy violations, incorrect tool usage, poor communication). |
| **0.0** | Response is invalid due to critical policy violations, incorrect tool calls, or complete failure to address the request. |
### Invalid Response Criteria (score = 0.0)
A response is invalid if it meets at least one of the following conditions:
- Fails to authenticate user at conversation start
- Attempts to help multiple users or wrong user
- Modifies database without explicit user confirmation
- Makes multiple tool calls in single response when responding to user
- Fabricates information not provided by tools
- Uses incorrect tool or parameters that would cause operation failure
- Completely ignores or misunderstands the customer request
Grader User Template
## Evaluation Data
### Message History
{{ item.messages }}
### Reference Response
{% if item.reference_tools is defined and item.reference_tools %}
**Tools**
{{ item.reference_tools }}
{% endif %}
{% if item.reference_text is defined and item.reference_text %}
**Text**
{{ item.reference_text }}
{% endif %}
### Model Response
**Tools**
{{ sample.output_tools }}
**Text**
{{ sample.output_text }}

More Details: Agent Config and Replication Commands

Discussion

The results paint a nuanced picture. RFT delivered performance gains for data extraction and agentic coding, but degraded customer service performance relative to baseline. Only in agentic coding did RFT clearly outperform SFT, but at 241x the cost.

It is interesting to see performance gains on agentic coding with a small amount of training data as presumably OpenAI optimized o4-mini specifically for this task. For all tasks, evaluating how downstream performance scales with training data size would provide useful information to LLM application engineers.

The freedom to design grader functions is an attractive feature of RFT. Evaluating the sensitivity of fine-tuned model performance to variations on model grader prompts or the addition of programmatic graders is an interesting future direction.

The nuanced results demand further analysis, but we are limited by two important factors: cost and content moderation.

Bottleneck: Cost

RFT costs present a real barrier for practitioners actively iterating on LLMs.

At hundreds or thousands of dollars per experiment, extensive hyperparameter search becomes prohibitively expensive. The grader configuration alone presents a high dimensional space of factors that could affect results, but systematic exploration would cost tens of thousands of dollars. API-based fine-tuning methods are attractive because users don’t need their own GPU resources, but the cost of RFT undermines this appeal.

Without the ability to iterate and experiment, it’s difficult to know if poor results come from the method itself or suboptimal configuration. For example, why do we see a performance drop on customer service? We’d like to answer such questions by enriching our analysis so that engineers can make informed decisions, but the cost would be prohibitive at scale. More efficient reinforcement learning techniques (like amortizing advantage estimation through learned value functions) could also make reinforcement learning a more viable optimization solution for LLM applications.

Bottleneck: Content Moderation

RFT appears to trigger much stricter content moderation than SFT or inference.

We had to try multiple random dataset splits to find subsets of data that wouldn’t be flagged, despite these being standard public benchmarks that work fine with SFT. Moreover, the datasets that were flagged did not appear to violate OpenAI’s usage policies in any meaningful way. For example, the data extraction dataset only contains text that is typically found in major newspapers.

The presence of examples that trigger policy violations makes it impossible for the full training sets to pass validation. So even if the considerable investment were made, we can’t run RFT using all available examples at present. We have shared example datasets with OpenAI for clarification.

Programmatic SFT and RFT using TensorZero

10.3KTensorZero provides an open-source stack for building industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation — everything you need to systematically fine-tune models for production use cases.

  1. Set up an LLM gateway: Integrate the TensorZero Gateway to access every LLM provider through a unified API (<1ms p99 latency).

  2. Collect quality data: Configure feedback collection to curate successful conversations, automatically stored in your database. Easily export curated datasets for further analysis or optimization.

  3. Train and evaluate: Run SFT and RFT jobs with multiple hyperparameter configurations programmatically. (See our earlier blog post: Distillation Programmatic Data Curation: Smarter LLMs, 5-30x Cheaper Inference) Optionally, run evaluations to identify the best performers.

    Example: Reinforcement Fine-Tuning for o4-mini on terminal-bench

    The script below demonstrates how to fine-tune o4-mini on terminal-bench using TensorZero’s programmatic API.

    import os
    from time import sleep
    from tensorzero import (
    TensorZeroGateway,
    BooleanMetricFilter,
    GCPVertexGeminiSFTConfig,
    OptimizationJobStatus,
    )
    CONFIG_PATH = "<your-config-path>/config/tensorzero.toml"
    # Function to optimize, e.g. "terminal_agent"
    FUNCTION_NAME = "terminal_agent"
    # Model to get demonstrations from
    VARIANT_NAME = "o4-mini"
    # Model to reinforcement fine-tune
    FT_MODEL = "o4-mini"
    # Model to grade responses
    GRADER_MODEL = "o4-mini"
    # URL to ClickHouse database that stores your inferences
    CLICKHOUSE_URL = "<your-clickhouse-url>"
    # Build a TensorZero client
    client = TensorZeroGateway.build_embedded(
    config_file=CONFIG_PATH,
    clickhouse_url=CLICKHOUSE_URL,
    )
    # Filter for successful conversations
    filters = BooleanMetricFilter(
    metric_name="resolved",
    value=True,
    )
    # List high-quality conversations
    inferences = client.experimental_list_inferences(
    function_name=FUNCTION_NAME,
    variant_name=VARIANT_NAME,
    filters=filters,
    output_source="inference",
    )
    # Render the conversations for fine-tuning
    rendered_samples = client.experimental_render_samples(
    stored_samples=inferences,
    variants={FUNCTION_NAME: VARIANT_NAME},
    )
    # Define a grader
    grader_system_template = """
    You are an expert grader of model-generated command-line task solutions in Linux environments.
    In the evaluation data section below, you will be given ...
    [ADD GRADER INSTRUCTIONS]
    Evaluate and assign a single float score (0.0-1.0) based on the criteria below:
    [ADD GRADER INSTRUCTIONS]
    """
    grader_user_template = """
    ## Evaluation Data
    ### Message History
    {{ item.messages }}
    ### Reference Answer
    {% if item.reference_tools is defined and item.reference_tools %}
    **Tools**
    {{ item.reference_tools }}
    {% endif %}
    {% if item.reference_text is defined and item.reference_text %}
    **Text**
    {{ item.reference_text }}
    {% endif %}
    ### Model Answer
    **Tools**
    {{ sample.output_tools }}
    **Text**
    {{ sample.output_text }}
    """
    grader = {
    "type": "score_model",
    "name": f"{FUNCTION_NAME}_grader",
    "input": [
    {
    "role": "developer",
    "content": grader_system_template,
    },
    {
    "role": "user",
    "content": grader_user_template,
    },
    ],
    "model": GRADER_MODEL,
    "range": [0, 1],
    }
    # Configure the fine-tuning job
    optimization_config = GCPVertexGeminiSFTConfig(
    model=FT_MODEL,
    grader=grader,
    reasoning_effort="low",
    )
    # Launch the fine-tuning job
    optimization_job_handle = client.experimental_launch_optimization(
    train_samples=rendered_samples,
    val_samples=None, # Optionally, create a validation split
    optimization_config=optimization_config,
    )
    # Poll the fine-tuning job until it completes
    while True:
    job_info = client.experimental_poll_optimization(
    job_handle=optimization_job_handle
    )
    if job_info.status == OptimizationJobStatus.Completed:
    break
    sleep(30) # Poll every 30 seconds for production jobs
  4. Deploy with confidence: Leverage the experimentation features to gradually route traffic to your fine-tuned models, starting with a small allocation and scaling based on performance metrics.

So, is reinforcement fine-tuning worth it?

For most use cases, not yet.

At 100-700x the cost of supervised fine-tuning, we only observed RFT to deliver clear wins on agentic coding tasks. Data extraction performed better for less with SFT, and customer service actually got worse with RFT.

The technology shows promise. It gives engineers flexibility in reward design through flexible grader configurations and the performance gains can be substantial when it works. But between the prohibitive costs, content moderation hurdles, and limited applicability, RFT is a difficult choice to justify. Until costs come down dramatically or best practices are established across task types, supervised fine-tuning remains a pragmatic alternative in most cases for LLM application improvement.

But, if you have a use case that benefits from reasoning (e.g. agentic coding) or sufficient budget, RFT may deliver the performance edge you need.


Appendix: Cost Estimation Methodology

We estimate the cost of RFT on full training datasets by extrapolating from our small-scale experiments. Since OpenAI’s pricing is time-based ($100/hour) plus token-based grading costs, we needed to estimate both components.

Table 1: Actual RFT Costs (Small-Scale Experiments)

Task Training Configuration Grader Usage Cost Breakdown
n Epochs Batch Time Steps Step Time Input Output Cached Training Grader Total
CoNLL++ NER 10 3 2 30m 15 2.00m 7.3M 1.6M N/A $50.00 $15.07 $65.07
terminal-bench 17 3 3 79m 17 4.65m 19.0M 2.5M 18.2M $131.67 $36.91 $168.57
τ-bench retail 23 2 4 91m 11.5 7.91m 25.2M 3.6M 24.1M $151.67 $50.19 $201.85

Actual costs from our RFT experiments using minimal training data. Training cost is based on OpenAI's $100/hour rate. Grader costs are $1.10 per M input tokens, $4.40 per M output tokens, and $0.275 per M cached tokens.

Table 2: Estimated RFT Costs (Full Training Data)

Task Training Configuration Grader Usage Cost Breakdown
n Epochs Batch Time Steps Step Time Input Output Cached Training Grader Total
CoNLL++ NER 127 1 6 42m 21 2.00m 30.9M 6.8M N/A $70.56 $63.80 $134.35
terminal-bench 237 1 8 138m 30 4.65m 88.3M 11.6M 84.6M $229.45 $171.50 $400.95
τ-bench retail 3866 1 8 64h 483 7.91m 2.1B 0.3B 2.0B $6,373.30 $4,217.93 $10,591.23

Estimated costs for RFT on full datasets.

Estimation Approach

For training cost, we assume training time\text{time} scales linearly with the number of gradient steps. We know the number of inferences n\mathrm{n} and OpenAI determines epochs\text{epochs} and batch-size\text{batch-size} prior to full dataset RFT failure:

steps=nepochsbatch-size\text{steps} = \frac{\mathrm{n} * \text{epochs}}{\text{batch-size}}

We assume that step-time0\text{step-time}_0 is unchanged and estimate:

time=step-time0steps.\text{time} = \text{step-time}_0 * \text{steps}.

For grader cost, we assume each inference requires the same average token usage so that grader usage scales with the number of inferences nepochs\mathrm{n} * \text{epochs}:

tokens=tokens0nepochsn0epochs0.\text{tokens} = \frac{\text{tokens}_0 * \mathrm{n} * \text{epochs}}{\mathrm{n}_0 * \text{epochs}_0}.

Acknowledgements: Thanks to Viraj Mehta, Gabriel Bianconi, Alan Mishler, and Shuyang Li for helpful feedback.


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