qwen3-4b-instruct-2507-retool-grpo

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

Results (final checkpoint, step 270)

Evaluated with the code interpreter at 16k response budget; AIME scores are mean accuracy over 16 samples/problem.

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.