From 75b3d4875f4b520124fe5db1b246cf4f78607ffd Mon Sep 17 00:00:00 2001
From: ModelHub XC <noreply@modelhub.org.cn>
Date: Tue, 26 May 2026 22:20:12 +0800
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Model: Kwai-Klear/qwen2.5-math-rlep
Source: Original Platform
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diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+  - Kwai-Klear/RLEP_dataset
+  - BytedTsinghua-SIA/DAPO-Math-17k
+base_model: Qwen/Qwen2.5-Math-7B
+---
+
+# RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
+
+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).
+
+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.
+
+[[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)]
+
+<p align="center">
+  <img src="https://github.com/Kwai-Klear/RLEP/raw/main/image/rlep_method.png" width="85%" alt="RLEP Method Overview">
+</p>
+
+## ✨ Key Highlights
+
+*   **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.
+*   **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.
+
+<p align="center">
+  <img src="https://github.com/Kwai-Klear/RLEP/raw/main/image/exp_acc.png" width="85%" alt="RLEP Experimental Accuracy">
+</p>
+
+## 🚀 Quick Start (Inference)
+
+Here’s a simple example of running inference with vLLM.
+First, install vLLM (version ≥ 0.7.3):
+```bash
+pip3 install vllm
+```
+After installation, you can load and run the model in your Python code like this:
+
+```python
+import os
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_path = 'Kwai-Klear/qwen2.5-math-rlep'
+sampling_params = SamplingParams(temperature=1.0, top_p=1.0, max_tokens=1024 * 3, n=1)
+llm = LLM(
+    model=model_path,
+    enforce_eager=False,
+    tensor_parallel_size=1,
+    seed=0,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+question = '''Find the sum of all integer bases $b>9$ for which $17_b$ is a divisor of $97_b.$'''
+
+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"
+post_fix = '\n\nRemember to put your answer on its own line after "Answer:".'
+question_with_instruct = prefix + question + post_fix  # the model is trained with this instruct. 
+messages = [{'content': question_with_instruct, 'role':'user'}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+output =llm.generate([text], sampling_params)[0]
+answer = output.outputs[0].text
+
+print(question)
+print(answer)
+
+```
+
+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).
+
+## Evaluation Results
+
+We evaluated the converged RLEP model at 320 training steps and the DAPO-nodyn-bs64 baseline at 400 steps.
+
+|                   | AIME-2024 | AIME-2025 | AMC-2023 |
+|-------------------|-----------|-----------|----------|
+| DAPO              | 32.6      | 18.9      | 77.5     |
+| DAPO-nodyn-bs64   | 37.4      | 19.4      | 77.3     |
+| **RLEP**          | **38.5**  | **21.3**  | **83.0** |
+
+## Citation
+
+If you find our paper or code helpful, we would appreciate it if you could cite our work:
+
+```bibtex
+@misc{zhang2025rlepreinforcementlearningexperience,
+      title={RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning}, 
+      author={Hongzhi Zhang and Jia Fu and Jingyuan Zhang and Kai Fu and Qi Wang and Fuzheng Zhang and Guorui Zhou},
+      year={2025},
+      eprint={2507.07451},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.07451}, 
+}
+```
+
+## Acknowledgement 
+
+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/). 
+Many thanks to the open-sourced works and the broader community for making these resources available!
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diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..17305b3
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
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+    "<|vision_start|>",
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+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  }
+}
diff --git a/tokenizer.json b/tokenizer.json
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--- /dev/null
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+size 11421896
diff --git a/tokenizer_config.json b/tokenizer_config.json
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--- /dev/null
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+    "<|quad_start|>",
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+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
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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
+}
diff --git a/vocab.json b/vocab.json
new file mode 100644
index 0000000..6c49fc6
--- /dev/null
+++ b/vocab.json
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