Qwen3-1.7B-Base-OPD

<h1 align="center">Qwen3-1.7B-Base-OPD</h1> <div align="center" style="line-height: 1;"> <a href="https://arxiv.org/abs/2604.13016" style="margin: 2px;"> <img alt="Paper" src="https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://github.com/thunlp/OPD" style="margin: 2px;"> <img alt="Github" src="https://img.shields.io/badge/OPD-000000?style=for-the-badge&logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/papers/2604.13016" style="margin: 2px;"> <img alt="HF Papers" src="https://img.shields.io/badge/HF--Paper-%23FFD14D?style=for-the-badge&logo=huggingface&logoColor=black" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/lllyx/Qwen3-4B-Base-GRPO" style="margin: 2px;"> <img alt="Teacher" src="https://img.shields.io/badge/Teacher-Qwen3--4B--Base--GRPO-blue?style=for-the-badge&logo=huggingface&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://x.com/HBX_hbx/status/2044464414829777354" style="margin: 2px;"> <img alt="Twitter" src="https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <br>

Qwen3-1.7B-Base-OPD is an on-policy distillation (OPD) checkpoint initialized from Qwen3-1.7B-Base. It is distilled from the teacher model Qwen3-4B-Base-GRPO using the DAPO-Math-17k dataset, and is intended 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 on-policy distillation (OPD) to Qwen3-1.7B-Base, with Qwen3-4B-Base-GRPO serving as the teacher model. The OPD training uses DAPO math prompts/data and is designed to transfer the teacher's math-focused reasoning behavior into a smaller 1.7B-parameter student model.

Key characteristics

• Student/base model: Qwen3-1.7B-Base
• Teacher model: lllyx/Qwen3-4B-Base-GRPO
• Training data: DAPO-Math-17k
• Training stage: On-Policy Distillation (OPD)
• Training framework: verl
• Rollout engine: vLLM
• Primary domain: Mathematical reasoning
• Model architecture: Qwen3ForCausalLM
• Precision: bfloat16
• Context length: 32768 tokens

Training Details

Training configuration

• Base checkpoint: Qwen/Qwen3-1.7B-Base
• Teacher checkpoint: lllyx/Qwen3-4B-Base-GRPO
• Training framework: verl
• Training method: on-policy distillation with GRPO-style rollouts
• Distillation loss mode: k1
• Policy-gradient term: enabled
• Training dataset: DAPO-Math-17k/DAPO-Math.parquet
• Primary task domain: math reasoning
• Chat template thinking mode: disabled (enable_thinking=False)
• Model type: qwen3

Rollout and optimization

• Rollout engine: vLLM
• Responses per prompt: 4
• Prompt length: 1024
• Response length: 7168
• Max rollout model length: 8193
• Train batch size: 64
• PPO mini-batch size: 16
• PPO micro-batch size per GPU: 1
• Max PPO token length per GPU: 8192
• Actor learning rate: 1e-6
• Total epochs: 1
• Save frequency: every 20 steps

Runtime setup

• Distributed backend: Ray
• Number of nodes: 1
• GPUs per node: 4
• Teacher world size: 4
• Rollout tensor parallel size: 1
• Teacher tensor parallel size: 1
• Actor training: FSDP with parameter and optimizer offload
• Gradient checkpointing: enabled
• Padding removal: enabled
• Torch compile for actor: enabled
• Reward function: rule-based math reward from verl/recipe/r1_ascend/deepscaler.py::compute_score

Dataset

• Training data: BytedTsinghua-SIA/DAPO-Math-17k
• Teacher rollout/source model: lllyx/Qwen3-4B-Base-GRPO
• Student initialization: Qwen/Qwen3-1.7B-Base

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "lllyx/Qwen3-1.7B-Base-OPD"

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:

@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}
}