You can deploy this model and start earning money today!

lllyx/
Qwen3-4B-Base-GRPO

Name: Qwen3-4B-Base-GRPO
Availability: InStock

reinforcement-learning

on-policy-distillation

Views

No views yet

Qwen3-4B-Base-GRPO

Qwen3-4B-Base-GRPO is a post-RL checkpoint trained with the verl framework. It starts from Qwen3-4B-Base and applies GRPO on the DAPO-Math-17k-Processed dataset for mathematical reasoning and problem-solving.

This model is associated with the paper:
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Paper link: https://arxiv.org/abs/2604.13016

Model Description

This model is obtained by applying GRPO reinforcement learning to Qwen3-4B-Base with verl. The training is intended to improve math-focused reasoning performance under the on-policy distillation setting.

Key characteristics

Base model: Qwen3-4B-Base
Training framework: verl
Training stage: Reinforcement Learning (GRPO)
Parameter update: Full-parameter actor update
Primary domain: Mathematical reasoning
Reward model: Not used (reward_model.enable: false)
Rollout engine: vLLM
Context length: 32768 tokens
Responses per prompt: 8

Training Details

Training configuration

Framework: verl
Algorithm: grpo
GRPO outcome weight: 1.0
Learned reward model: disabled (reward_model.enable: false)
Reward source: custom rule-based math reward function
Training dataset: DAPO-Math-17k-Processed
Training file: datasets/DAPO-Math-17k-Processed/DAPO-Math.parquet
Validation datasets: AIME25, AMC23, AIME24
Prompt length: 1024
Response length: 7168
Validation response length: 31744
Max model length: 32768
Rollout temperature: 1.0
Repetition penalty: 1.0
KL loss: disabled
Format reward: disabled
Loss aggregation: token-mean
Learning rate: 1e-6
PPO mini-batch size: 64
PPO micro-batch size per GPU: 1
Tensor parallel size: 1
Number of GPUs: 8
Number of epochs: 1
Save frequency: every 20 steps
Test frequency: every 20 steps

Dataset

Training dataset: DAPO-Math-17k-Processed
Validation datasets: AIME25, AMC23, AIME24

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "lllyx/Qwen3-4B-Base-GRPO"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Citation

If you use this model, please consider citing the related paper:

bibtex

@article{li2026rethinking,
  title={Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe},
  author={Li, Yaxuan and Zuo, Yuxin and He, Bingxiang and Zhang, Jinqian and Xiao, Chaojun and Qian, Cheng and Yu, Tianyu and Gao, Huan-ang and Yang, Wenkai and Liu, Zhiyuan and Ding, Ning},
  journal={arXiv preprint arXiv:2604.13016},
  year={2026}
}