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Model: Kwai-Klear/qwen2.5-math-rlep Source: Original Platform
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---
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- Kwai-Klear/RLEP_dataset
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- BytedTsinghua-SIA/DAPO-Math-17k
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base_model: Qwen/Qwen2.5-Math-7B
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---
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# RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
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This repository contains the `qwen2.5-math-rlep` model, which is a key checkpoint from the RLEP training process based on Qwen2.5-Math-7B, as presented in the paper [RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning](https://huggingface.co/papers/2507.07451).
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Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights. **RLEP** -- Reinforcement Learning with Experience rePlay -- is a two-phase framework that first collects verified trajectories and then replays them during subsequent training. At every update step, the policy is optimized on mini-batches that blend newly generated rollouts with these replayed successes. By replaying high-quality examples, RLEP steers the model away from fruitless exploration, focuses learning on promising reasoning paths, and delivers both faster convergence and stronger final performance.
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[[Paper](https://huggingface.co/papers/2507.07451)] [[Code](https://github.com/Kwai-Klear/RLEP)] [[Checkpoints](https://huggingface.co/Kwai-Klear/qwen2.5-math-rlep)] [[Dataset](https://huggingface.co/datasets/Kwai-Klear/RLEP_dataset)]
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<p align="center">
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<img src="https://github.com/Kwai-Klear/RLEP/raw/main/image/rlep_method.png" width="85%" alt="RLEP Method Overview">
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</p>
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## ✨ Key Highlights
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* **Rapid early gains**: On AIME-2024 RLEP hits the baseline’s peak accuracy by step 135 (the baseline needs 380). On AIME-2025 it surpasses the baseline’s best score after only 50 steps.
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* **Higher final performance**: RLEP ultimately lifts the peak accuracy from 38.2% → 39.9% (AIME-2024), 19.8% → 22.3% (AIME-2025), and 77.0% → 82.2% on AMC-2023 benchmark.
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<p align="center">
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<img src="https://github.com/Kwai-Klear/RLEP/raw/main/image/exp_acc.png" width="85%" alt="RLEP Experimental Accuracy">
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</p>
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## 🚀 Quick Start (Inference)
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Here’s a simple example of running inference with vLLM.
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First, install vLLM (version ≥ 0.7.3):
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```bash
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pip3 install vllm
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```
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After installation, you can load and run the model in your Python code like this:
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```python
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from vllm import LLM, SamplingParams
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model_path = 'Kwai-Klear/qwen2.5-math-rlep'
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sampling_params = SamplingParams(temperature=1.0, top_p=1.0, max_tokens=1024 * 3, n=1)
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llm = LLM(
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model=model_path,
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enforce_eager=False,
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tensor_parallel_size=1,
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seed=0,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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question = '''Find the sum of all integer bases $b>9$ for which $17_b$ is a divisor of $97_b.$'''
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prefix="Solve the following math problem step by step. The last line of your response should be of the form Answer: $Answer (without quotes) where $Answer is the answer to the problem.\n\n"
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post_fix = '\n\nRemember to put your answer on its own line after "Answer:".'
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question_with_instruct = prefix + question + post_fix # the model is trained with this instruct.
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messages = [{'content': question_with_instruct, 'role':'user'}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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output =llm.generate([text], sampling_params)[0]
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answer = output.outputs[0].text
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print(question)
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print(answer)
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```
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To evaluete the model on benchmarks like AIME-2024, AIME-2025 and AMC-2023 etc. please refer to [our repo](http://github.com/Kwai-Klear/RLEP?tab=readme-ov-file#evaluation).
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## Evaluation Results
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We evaluated the converged RLEP model at 320 training steps and the DAPO-nodyn-bs64 baseline at 400 steps.
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| | AIME-2024 | AIME-2025 | AMC-2023 |
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|-------------------|-----------|-----------|----------|
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| DAPO | 32.6 | 18.9 | 77.5 |
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| DAPO-nodyn-bs64 | 37.4 | 19.4 | 77.3 |
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| **RLEP** | **38.5** | **21.3** | **83.0** |
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## Citation
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If you find our paper or code helpful, we would appreciate it if you could cite our work:
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```bibtex
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@misc{zhang2025rlepreinforcementlearningexperience,
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title={RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning},
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author={Hongzhi Zhang and Jia Fu and Jingyuan Zhang and Kai Fu and Qi Wang and Fuzheng Zhang and Guorui Zhou},
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year={2025},
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eprint={2507.07451},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.07451},
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}
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```
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## Acknowledgement
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We conducted our experiments with the [VERL](https://github.com/volcengine/verl) framework and the [Qwen2.5-7B-Math](https://huggingface.co/Qwen/Qwen2.5-Math-7B) model, using the dataset and training scripts provided by [DAPO](https://dapo-sia.github.io/).
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Many thanks to the open-sourced works and the broader community for making these resources available!
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'Please reason step by step, and put your final answer within \\\\boxed{}.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nPlease reason step by step, and put your final answer within \\\\boxed{}.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user