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Model: BillyWang1/qwen3-4b-instruct-2507-retool-grpo
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- reinforcement-learning
+- grpo
+- tool-use
+- code-interpreter
+- math
+- retool
+- slime
+---
+
+# 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](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) |
+| Algorithm | GRPO (group-normalized outcome advantages, no KL penalty) |
+| Framework | [slime](https://github.com/THUDM/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{...}.
+
+```python
+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.