youngzhong/SOD-GRPO_teacher-4B
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<h1>SOD-GRPO_teacher-4B</h1>
<p> <a href="https://arxiv.org/abs/2605.07725"> <img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=red" alt="Paper on arXiv"/> </a> <a href="https://github.com/YoungZ365/SOD"> <img src="https://img.shields.io/badge/Code-GitHub-black?logo=github&logoColor=white" alt="Code on GitHub"/> </a> <a href="https://huggingface.co/collections/youngzhong/sod-6a03530369d76913c24a4ffb"> <img src="https://img.shields.io/badge/Collection-SOD-yellow?logo=huggingface" alt="HuggingFace Collection"/> </a> <a href="https://huggingface.co/papers/2605.07725"> <img src="https://img.shields.io/badge/Daily%20Paper-SOD-yellow?logo=huggingface&logoColor=yellow" alt="HuggingFace Daily Paper"/> </a> <a href="https://www.alphaxiv.org/abs/2605.07725"> <img src="https://img.shields.io/badge/alphaXiv-2605.07725-purple?logo=arxiv&logoColor=white" alt="alphaXiv"/> </a> </p>
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About
SOD-GRPO_teacher-4B is a 4B agentic reasoning model trained with GRPO (Group Relative Policy Optimization), serving as the teacher model in the SOD distillation framework.
This model is used to distill smaller student models (SOD-0.6B and SOD-1.7B) via the SOD method, which introduces adaptive step-level weighting to handle cascading error propagation in tool-integrated reasoning.
Model Information
Related Models
Performance
We report average@32 over 5 runs on challenging math, science, and code benchmarks.
Distilled Students
Acknowledgement
We sincerely thank the authors of DemyAgent-4B and the paper "Demystifying Reinforcement Learning in Agentic Reasoning" (arXiv:2510.11701) for their contribution.
Citation
@article{zhong2026sod,
title={SOD: Step-wise On-policy Distillation for Small Language Model Agents},
author={Zhong, Qiyong and Zheng, Mao and Song, Mingyang and Lin, Xin and Sun, Jie and Jiang, Houcheng and Wang, Xiang and Fang, Junfeng},
journal={arXiv preprint arXiv:2605.07725},
year={2026}
}