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Model: RoadQAQ/ReLIFT-Qwen2.5-Math-1.5B-Zero Source: Original Platform
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---
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license: cc-by-nc-4.0
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library_name: transformers
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pipeline_tag: question-answering
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---
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# Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
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This repository contains the models introduced in the paper [Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions](https://huggingface.co/papers/2506.07527).
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## Paper Abstract
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Recent advances in large language model (LLM) reasoning have shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL). However, despite these successes, RL in its current form remains insufficient to induce capabilities that exceed the limitations of the base model, as it is primarily optimized based on existing knowledge of the model rather than facilitating the acquisition of new information. To address this limitation, we employ supervised fine-tuning (SFT) to learn what RL cannot, which enables the incorporation of new knowledge and reasoning patterns by leveraging high-quality demonstration data. We analyze the training dynamics of RL and SFT for LLM reasoning and find that RL excels at maintaining and improving performance on questions within the model's original capabilities, while SFT is more effective at enabling progress on questions beyond the current scope of the model. Motivated by the complementary strengths of RL and SFT, we introduce a novel training approach, \textbf{ReLIFT} (\textbf{Re}inforcement \textbf{L}earning \textbf{I}nterleaved with Online \textbf{F}ine-\textbf{T}uning). In ReLIFT, the model is primarily trained using RL, but when it encounters challenging questions, high-quality solutions are collected for fine-tuning, and the training process alternates between RL and fine-tuning to enhance the model's reasoning abilities. ReLIFT achieves an average improvement of over +5.2 points across five competition-level benchmarks and one out-of-distribution benchmark compared to other zero-RL models. Furthermore, we demonstrate that ReLIFT outperforms both RL and SFT while using only 13\% of the detailed demonstration data, highlighting its scalability. These results provide compelling evidence that ReLIFT overcomes the fundamental limitations of RL and underscores the significant potential.
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## Code and Project Page
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https://github.com/TheRoadQaQ/ReLIFT
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## Sample Usage
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(Inference example from the GitHub README can be pasted here)
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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="RoadQAQ/ReLIFT-Qwen2.5-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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config.json
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}
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207
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"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{{ bos_token }}Your task is to follow a systematic, thorough reasoning process before providing the final solution. This involves analyzing, summarizing, exploring, reassessing, and refining your thought process through multiple iterations. Structure your response into two sections: Thought and Solution. In the Thought section, present your reasoning using the format: “<think>\n {thoughts} </think>\n”. Each thought should include detailed analysis, brainstorming, verification, and refinement of ideas. After “</think>\n,” in the Solution section, provide the final, logical, and accurate answer, clearly derived from the exploration in the Thought section. If applicable, include the answer in \boxed{} for closed-form results like multiple choices or mathematical solutions. {% for message in messages %}{% if message['role'] == 'user' %}User: This is the problem:\n{{ message['content'] }}\n{% elif message['role'] == 'assistant' %}Assistant: <think>\n{{ message['content'] }}\n{% endif %}{% endfor %}{% if add_generation_prompt %}Assistant: <think>\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user