Elliott
/

LUFFY-Qwen-Math-1.5B-Zero

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+base_model:
+- Qwen/Qwen2.5-Math-1.5B
 library_name: transformers
+license: mit
+pipeline_tag: text-generation
+tags:
+- reasoning
+- Zero-RL
 ---
+# 📖Introduction
+![Github](https://img.shields.io/badge/LUFFY-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white)
+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.
+### Key Highlights:
+- **Off-Policy Guidance:** Seamlessly integrates external reasoning traces to bootstrap learning from stronger models.
+- **Dynamic Balance:** Learns when to imitate and when to explore, adapting over the course of training.
+- **Policy Shaping:** Emphasizes important actions often ignored in standard policy gradients, enabling better generalization.
+---
+## Inference
+Here’s an example of using LUFFY for inference:
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+model_path="Elliott/LUFFY-Qwen-Math-7B-Zero"
+question = "which number is larger? 9.11 or 9.9?"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+messages = [{"role": "user", "content": question}]
+chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+llm = LLM(model=model_path)
+params = SamplingParams(temperature=0.6, max_tokens=8192)
+outputs = llm.generate([chat], params)
+print(outputs[0].outputs[0].text)
+```
+---
+# 📃Evaluation
+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:
+| **Model** | **AIME 24** | **AIME 25** | **AMC** | **MATH-500** | **Minerva** | **Olympiad** | **Avg.** |
+|-------|---------|---------|-----|----------|---------|----------|------|
+| Qwen2.5-Math-1.5B-Base | 7.9 | 4.7 | 26.4 | 31.0 | 12.1 | 21.5 | 17.3 |
+| Qwen2.5-Math-1.5B-Instruct | 11.4 | 8.5 | 47.4 | 75.2 | 27.6 | 38.7 | 34.8 |
+| SFT | 15.2 | **14.3** | 43.5 | 74.8 | **30.9** | 36.9 | 40.3 |
+| On-Policy RL | 12.6 | 6.5 | 42.6 | 68.8 | 22.1 | 34.4 | 36.1 |
+| **LUFFY-1.5B-Zero** | **15.2** | 12.7 | **46.8** | **79.4** | 26.5 | **42.4** | **42.1** |
+---
+# 🌻Acknowledgement
+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.
+Code: https://github.com/ElliottYan/LUFFY
+# Citation
+If you find our model, data, or evaluation code useful, please kindly cite our paper:
+```bib
+@misc{luffy,
+      title={Learning to Reason under Off-Policy Guidance},
+      author={Jianhao Yan and Yafu Li and Zican Hu and Zhi Wang and Ganqu Cui and Xiaoye Qu and Yu Cheng and Yue Zhang},
+      year={2025},
+      eprint={2504.14945},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2504.14945},
+}
+```