103 lines
5.1 KiB
Markdown
103 lines
5.1 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-Math-7B
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library_name: transformers
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license: mit
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pipeline_tag: text-generation
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tags:
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- reasoning
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- Zero-RL
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---
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# 📖Introduction
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LUFFY is a reinforcement learning framework that bridges the gap between zero-RL and imitation learning by incorporating off-policy reasoning traces into the training process. Built upon GRPO, LUFFY combines on-policy rollouts with off-policy demonstrations during advantage estimation and introduces **policy shaping** via regularized importance sampling to emphasize low-probability yet crucial actions.
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### Key Highlights:
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- **Off-Policy Guidance:** Seamlessly integrates external reasoning traces to bootstrap learning from stronger models.
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- **Dynamic Balance:** Learns when to imitate and when to explore, adapting over the course of training.
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- **Policy Shaping:** Emphasizes important actions often ignored in standard policy gradients, enabling better generalization.
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---
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## Inference
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Here’s an example of using LUFFY for inference:
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_path="Elliott/LUFFY-Qwen-Math-7B-Zero"
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question = "which number is larger? 9.11 or 9.9?"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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messages = [{"role": "user", "content": question}]
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chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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llm = LLM(model=model_path)
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params = SamplingParams(temperature=0.6, max_tokens=8192)
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outputs = llm.generate([chat], params)
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print(outputs[0].outputs[0].text)
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```
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---
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# 📃Evaluation
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LUFFY is evaluated on six competition-level benchmarks, achieving state-of-the-art results among all zero-RL methods. It surpasses both on-policy RL and imitation learning (SFT), especially in generalization:
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| **Model** | **AIME 2024** | **AIME 2025** | **AMC** | **MATH-500** | **Minerva** | **Olympiad** | **Avg.** |
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|-----------------------------------|-------------|-------------|---------|---------------|-------------|---------------|----------|
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| Qwen2.5-Math | 12.9 | 4.2 | 32.6 | 48.8 | 10.7 | 14.8 | 20.7 |
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| Qwen2.5-Math-Instruct | 11.4 | 8.8 | 48.3 | 81.2 | 33.1 | 38.8 | 36.9 |
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| SimpleRL-Zero | 26.3 | 6.7 | 55.4 | 74.4 | 25.7 | 35.4 | 37.3 |
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| OpenReasoner-Zero | 17.2 | 15.0 | 52.3 | 84.6 | 33.8 | 47.1 | 41.7 |
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| PRIME-Zero | 17.9 | 14.7 | 55.2 | 79.4 | **38.2** | 42.2 | 41.3 |
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| Oat-Zero | **31.7** | 11.0 | 61.6 | 79.2 | 29.8 | 42.5 | 42.6 |
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| **LUFFY** | 29.5 | 23.2 | **66.1**| **88.4** | 33.8 | **56.4** | **49.6** |
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---
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LUFFY also generalizes well to out-of-distribution tasks, with over +6.2 average gain on ARC-C, GPQA, and MMLU-Pro.
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| **Model** | **ARC-c** | **GPQA-diamond** | **MMLU-Pro** | **Avg.** |
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|----------------------------------|-----------|------------------|--------------|----------|
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| Qwen2.5-Math-7B-Base | 18.2 | 11.1 | 16.9 | 15.4 |
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| Qwen2.5-Math-7B-Instruct | 70.3 | 24.7 | 34.1 | 43.0 |
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| SimpleRL-Zero | 30.2 | 23.2 | 34.5 | 29.3 |
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| PRIME-Zero | 73.3 | 18.2 | 32.7 | 41.4 |
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| Oat-Zero | 70.1 | 23.7 | 41.7 | 45.2 |
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| OpenReasoner-Zero | 66.2 | 29.8 | 58.7 | 51.6 |
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| **LUFFY** | _80.5_ | _39.9_ | **53.0** | **57.8** |
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---
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# 🌻Acknowledgement
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LUFFY builds upon [veRL](https://github.com/volcengine/verl) and [deepscaler](https://github.com/agentica-project/rllm), and utilizes [vLLM](https://github.com/vllm-project/vllm) for inference. We utilize [Math-Verify](https://github.com/huggingface/Math-Verify) for math reasoning evaluation. We thank the open-source community for datasets and backbones, including [NuminaMath](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT), [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k), [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), and [DeepSeek-R1](https://github.com/deepseek-ai/deepseek-r1) model.
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Code: https://github.com/ElliottYan/LUFFY
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# Citation
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If you find our model, data, or evaluation code useful, please kindly cite our paper:
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```bib
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@misc{luffy,
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title={Learning to Reason under Off-Policy Guidance},
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author={Jianhao Yan and Yafu Li and Zican Hu and Zhi Wang and Ganqu Cui and Xiaoye Qu and Yu Cheng and Yue Zhang},
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year={2025},
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eprint={2504.14945},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2504.14945},
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}
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``` |