92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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base_model: Qwen/Qwen3-4B
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tags:
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- agent
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- tool-use
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- reinforcement-learning
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- GRPO
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- math
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- code
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- reasoning
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pipeline_tag: text-generation
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---
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<div align="center">
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<h1>SOD-GRPO_teacher-4B</h1>
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<p>
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<a href="https://arxiv.org/abs/2605.07725">
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<img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=red" alt="Paper on arXiv"/>
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</a>
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<a href="https://github.com/YoungZ365/SOD">
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<img src="https://img.shields.io/badge/Code-GitHub-black?logo=github&logoColor=white" alt="Code on GitHub"/>
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</a>
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<a href="https://huggingface.co/collections/youngzhong/sod-6a03530369d76913c24a4ffb">
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<img src="https://img.shields.io/badge/Collection-SOD-yellow?logo=huggingface" alt="HuggingFace Collection"/>
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</a>
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<a href="https://huggingface.co/papers/2605.07725">
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<img src="https://img.shields.io/badge/Daily%20Paper-SOD-yellow?logo=huggingface&logoColor=yellow" alt="HuggingFace Daily Paper"/>
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</a>
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<a href="https://www.alphaxiv.org/abs/2605.07725">
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<img src="https://img.shields.io/badge/alphaXiv-2605.07725-purple?logo=arxiv&logoColor=white" alt="alphaXiv"/>
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</a>
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</p>
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</div>
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## About
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**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.
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This model is used to distill smaller student models ([SOD-0.6B](https://huggingface.co/youngzhong/SOD-0.6B) and [SOD-1.7B](https://huggingface.co/youngzhong/SOD-1.7B)) via the SOD method, which introduces adaptive step-level weighting to handle cascading error propagation in tool-integrated reasoning.
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## Model Information
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| Attribute | Value |
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|-----------|-------|
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| Base Model | [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
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| Training Pipeline | Cold-Start SFT → GRPO |
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| Parameters | 4B |
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## Related Models
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| Model | Description |
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|-------|-------------|
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| [SOD-0.6B](https://huggingface.co/youngzhong/SOD-0.6B) | SOD-distilled 0.6B student |
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| [SOD-1.7B](https://huggingface.co/youngzhong/SOD-1.7B) | SOD-distilled 1.7B student |
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| [SOD-GRPO_teacher-4B](https://huggingface.co/youngzhong/SOD-GRPO_teacher-4B) | GRPO-trained 4B teacher model (this model) |
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## Performance
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We report **average@32** over 5 runs on challenging math, science, and code benchmarks.
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| Method | AIME 2024 | AIME 2025 | GPQA-Diamond | LiveCodeBench-v6 | Average |
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|--------|-----------|-----------|--------------|------------------|---------|
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| **GRPO (This Model)** | **67.60** | **60.42** | **55.19** | **63.13** | **61.59** |
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### Distilled Students
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| Model | AIME 2024 | AIME 2025 | GPQA-Diamond | LiveCodeBench-v6 | Average |
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|-------|-----------|-----------|--------------|------------------|---------|
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| SOD-0.6B | 20.84 | 26.13 | 22.19 | 27.72 | 24.22 |
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| SOD-1.7B | 50.83 | 41.72 | 38.72 | 40.63 | 42.98 |
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## Acknowledgement
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We sincerely thank the authors of [DemyAgent-4B](https://huggingface.co/Gen-Verse/DemyAgent-4B) and the paper *"Demystifying Reinforcement Learning in Agentic Reasoning"* ([arXiv:2510.11701](https://arxiv.org/abs/2510.11701)) for their contribution.
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## Citation
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```bibtex
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@article{zhong2026sod,
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title={SOD: Step-wise On-policy Distillation for Small Language Model Agents},
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author={Zhong, Qiyong and Zheng, Mao and Song, Mingyang and Lin, Xin and Sun, Jie and Jiang, Houcheng and Wang, Xiang and Fang, Junfeng},
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journal={arXiv preprint arXiv:2605.07725},
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year={2026}
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}
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``` |