license, base_model, pipeline_tag, language, tags
| license | base_model | pipeline_tag | language | tags | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | Qwen/Qwen3-4B-Instruct-2507 | text-generation |
|
|
qwen3-4b-instruct-2507-retool-grpo
Qwen3-4B-Instruct-2507 trained with GRPO for tool-integrated math reasoning (ReTool-style code-interpreter RL).
This is the GRPO control run of a 26summer series comparing RL objectives (GRPO, GFlowRL, and process-reward variants) on identical data, seed, and infrastructure. The model interleaves natural-language reasoning with native Qwen3 code_interpreter tool calls (JSON tool-call format from the tokenizer's own chat template — no custom tags) and executes Python to verify intermediate steps before committing to a final boxed answer.
Training setup
| Base model | Qwen/Qwen3-4B-Instruct-2507 |
| Algorithm | GRPO (group-normalized outcome advantages, no KL penalty) |
| Framework | slime 0.3.0 (Megatron-LM training + SGLang rollouts, async RL) |
| Data | dapo-math-17k, 1 epoch = 271 rollouts / 1084 optimizer steps |
| Rollout geometry | 64 prompts × 16 samples per rollout, global batch 256 |
| Reward | rule-based ±1 on the final \boxed{...} answer |
| Tool | sandboxed Python code_interpreter, multi-turn, native chat-template tool calls |
| Max response length | 8192 (train) / 16384 (eval) |
| LR / seed | 1e-6 constant / 42 |
| Hardware | 1 node × 8 B200 (2 training + 6 inference GPUs, disaggregated) |
Results (final checkpoint, step 270)
Evaluated with the code interpreter at 16k response budget; AIME scores are mean accuracy over 16 samples/problem.
| benchmark | accuracy |
|---|---|
| AIME 2024 | 0.581 |
| AIME 2025 | 0.498 |
| MATH-500 | 0.956 |
Usage
Standard Qwen3 instruct usage; to reproduce the tool-use behavior, serve with a code_interpreter tool in the chat template and the training system prompt:
You are a helpful assistant that solves math problems step by step. You may call the code_interpreter tool to execute Python code whenever it helps your reasoning; use complete scripts including any imports. End your solution with the final answer in \boxed{...}.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("BillyWang1/qwen3-4b-instruct-2507-retool-grpo", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("BillyWang1/qwen3-4b-instruct-2507-retool-grpo")
The model was trained purely with RL on top of the instruct model — no SFT stage — so it retains the base model's general chat ability.