Elliott
/

LUFFY-Qwen-Math-7B-Zero

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----
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+---
+library_name: transformers
+tags:
+- reasoning
+- Zero-RL
+license: mit
+base_model:
+- Qwen/Qwen2.5-Math-7B
+pipeline_tag: text-generation
+---
+# 📖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 2024** | **AIME 2025** | **AMC** | **MATH-500** | **Minerva** | **Olympiad** | **Avg.** |
+|-----------------------------------|-------------|-------------|---------|---------------|-------------|---------------|----------|
+| Qwen2.5-Math                      | 12.9        | 4.2         | 32.6    | 48.8          | 10.7        | 14.8          | 20.7     |
+| Qwen2.5-Math-Instruct             | 11.4        | 8.8         | 48.3    | 81.2          | 33.1        | 38.8          | 36.9     |
+| SimpleRL-Zero                     | 26.3        | 6.7         | 55.4    | 74.4          | 25.7        | 35.4          | 37.3     |
+| OpenReasoner-Zero                 | 17.2        | 15.0        | 52.3    | 84.6          | 33.8        | 47.1          | 41.7     |
+| PRIME-Zero                        | 17.9        | 14.7        | 55.2    | 79.4          | **38.2**    | 42.2          | 41.3     |
+| Oat-Zero                          | **31.7**    | 11.0        | 61.6    | 79.2          | 29.8        | 42.5          | 42.6     |
+| SFT (Our repication)                               | 28.6        | **23.5**    | 59.0    | 86.0          | 37.5        | 51.1          | 47.6     |
+| On-Policy RL (Our repication)                      | 24.6        | 15.7        | 61.3    | 84.6          | 34.9        | 47.9          | 44.8     |
+| **LUFFY**                         | 29.5        | 23.2        | **66.1**| **88.4**      | 33.8        | **56.4**      | **49.6** |
+---
+LUFFY also generalizes well to out-of-distribution tasks, with over +6.2 average gain on ARC-C, GPQA, and MMLU-Pro.
+| **Model**                         | **ARC-c** | **GPQA-diamond** | **MMLU-Pro** | **Avg.** |
+|----------------------------------|-----------|------------------|--------------|----------|
+| Qwen2.5-Math-7B-Base             | 18.2      | 11.1             | 16.9         | 15.4     |
+| Qwen2.5-Math-7B-Instruct         | 70.3      | 24.7             | 34.1         | 43.0     |
+| SimpleRL-Zero                    | 30.2      | 23.2             | 34.5         | 29.3     |
+| OpenReasoner-Zero                | 66.2      | 29.8             | 58.7         | 51.6     |
+| PRIME-Zero                       | **73.3**  | 18.2             | 32.7         | 41.4     |
+| Oat-Zero                         | 70.1      | 23.7             | 41.7         | 45.2     |
+| SFT (Our repication)             | 75.2      | 24.7             | 42.7         | 47.5     |
+| On-Policy RL (Our repication)    | **82.3**  | **40.4**         | _49.3_       | _57.3_   |
+| **LUFFY**                        | _80.5_    | _39.9_           | **53.0**     | **57.8** |
+---
+# 🌻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.
+# 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},
+}
+```