license, base_model, pipeline_tag, language, tags

license	base_model	pipeline_tag	language	tags
apache-2.0	Qwen/Qwen3-4B-Instruct-2507	text-generation	en	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
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.