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Model: okwinds/Satori-7B-Round2 Source: Original Platform
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- text-generation
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---
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# 本模型论文解读，请看公众号文章 👇🏻
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### <img src="https://www.modelscope.cn/datasets/okwinds/Human-Like-DPO-Dataset/resolve/master/wechat.png" width="30" height="30" align="absmiddle"> 觉察流 - [Satori 带来 COAT：解锁大语言模型自省及推理潜能，告别教师模型](https://mp.weixin.qq.com/s/hnqv6YE4Cca2_lrJm5vcxg)
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> <span style="color:red;font-size:16px"> 声明：本模型完全转载自 Huggingface 上的 [Satori-reasoning/Satori-7B-Round2](https://huggingface.co/Satori-reasoning/Satori-7B-Round2) <br/>更多模型信息，请关注下文👇🏻， 为原数据集仓库的中文版说明。</span>
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<br/>
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#### _仓库作者在此 👇🏻 扫一扫_
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<img src="https://www.modelscope.cn/models/okwinds/GPT-2/resolve/master/qrcode_for_jcl_258.jpg" />
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# 下载方式
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('okwinds/Satori-7B-Round2')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/okwinds/Satori-7B-Round2.git
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```
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# 模型介绍
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**Satori-7B-Round2** 是一个基于开源模型（Qwen-2.5-Math-7B）和开源数据（OpenMathInstruct-2 和 NuminaMath）训练的 7B 大型语言模型（LLM）。**Satori-7B-Round2** 能够进行自回归搜索，即在没有外部指导的情况下进行自我反思和自我探索。这是通过我们提出的 Chain-of-Action-Thought（COAT）推理方法和两阶段后训练范式实现的。
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## 我们的方法
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我们将 LLM 推理表述为一个序贯决策问题，推理是一个逐步构建和优化答案的过程。具体来说，LLM（代理的策略）从输入上下文（初始状态）开始，生成一个推理步骤（动作），并更新上下文（下一个状态）。LLM 重复这一过程，直到得出最终答案，并接收一个评估最终答案是否与真实答案匹配的奖励。通过这种表述，我们可以使用强化学习（RL）训练 LLM 推理，目标是生成一系列推理步骤，以最大化预期奖励。
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### Chain-of-Action-Thought 推理（COAT）
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实现自回归搜索的关键挑战是使 LLM 能够在没有外部干预的情况下确定何时进行反思、继续或探索替代解决方案。为此，我们引入了几个特殊的元动作标记，以引导 LLM 的推理过程：
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- **继续推理**（<\|continue\|>）：鼓励 LLM 在当前推理轨迹的基础上生成下一个中间步骤。
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- **反思**（<\|reflect\|>）：提示模型暂停并验证之前推理步骤的正确性。
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- **探索替代解决方案**（<\|explore\|>）：信号模型识别推理中的关键缺陷并探索新的解决方案。
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我们将这种表述称为 Chain-of-Action-Thought（COAT）推理。每个 COAT 推理步骤是一个以其中一个元动作标记开头的标记序列。
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### 训练框架概述
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1. 一个小规模的格式调整（FT）阶段，帮助基础 LLM 内化 COAT 推理格式。
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2. 一个大规模的自我提升阶段，利用强化学习和“重启和探索”（RAE）技术。
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### 通过模仿学习进行格式调整
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这一阶段的目标是微调预训练的基础 LLM，使其模仿一些具有 COAT 推理格式的推理轨迹。为了合成包含尝试和错误的 COAT 轨迹，我们提出了一个多智能体数据合成框架，利用三个 LLM：
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- **生成器**：给定一个输入问题，生成器使用经典的 CoT 技术为给定输入问题生成多个推理路径。
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- **评论家**：评论家评估生成器生成的推理路径的正确性，提供反馈以优化推理并解决次优步骤。
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- **奖励模型**：奖励模型为优化后的推理路径分配分数，并选择最有效的路径作为最终演示轨迹。
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这三个模型协作构建高质量的演示轨迹。我们观察到，少量（10K）的演示轨迹足以让基础 LLM 遵循 COAT 推理格式。
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### 通过强化学习进行自我提升
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通过格式调整，LLM 已经采用了 COAT 推理风格，但在未见过的问题上泛化能力不足。RL 阶段旨在激励利用自我反思来提高推理的实际能力。我们从格式调整后的 LLM 开始，并进一步使用经典的 PPO 算法进行优化，同时采用两个关键策略：
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- **重启和探索（RAE）**：受 Go-Explore 的启发，我们训练 LLM 策略不仅从问题陈述开始推理，还从过去轨迹中采样的中间步骤开始推理，无论是正确的还是错误的。我们还添加了探索奖励，以鼓励更深入的反思，进一步增加策略得出正确答案的机会。
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- **迭代自我提升**：策略可能会收敛到局部次优解，无法进一步改进。受 Kickstarting 的启发，每轮 RL 训练后，我们将当前教师策略的知识通过监督微调蒸馏到学生模型（基础 LLM）中。从新微调的 LLM 开始，我们再进行一轮 RL 训练。
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**Satori-7B-Round2** 是通过第二轮迭代自我提升获得的。
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## 使用方法
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```python
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import os
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from tqdm import tqdm
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import torch
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from vllm import LLM, SamplingParams
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def generate(question_list, model_path):
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llm = LLM(
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model=model_path,
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trust_remote_code=True,
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tensor_parallel_size=1,
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)
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sampling_params = SamplingParams(
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max_tokens=4096,
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temperature=0.0,
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n=1,
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skip_special_tokens=True # 隐藏special tokens，如 "<\|continue\|>"、"<\|reflect\|>" 和 "<\|explore\|>"
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)
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outputs = llm.generate(question_list, sampling_params, use_tqdm=True)
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completions = [[output.text for output in output_item.outputs] for output_item in outputs]
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return completions
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def prepare_prompt(question):
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prompt = f"<|im_start|>user\n高效且清晰地解决以下数学问题。\n请逐步推理，并将最终答案放在 \\boxed{{}} 中。\n问题: {question}<|im_end|>\n<|im_start|>assistant\n"
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return prompt
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def run():
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model_path = "Satori-reasoning/Satori-7B-Round2"
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all_problems = [
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"哪个数字更大？9.11 还是 9.9？",
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]
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completions = generate(
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[prepare_prompt(problem_data) for problem_data in all_problems],
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model_path
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)
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for completion in completions:
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print(completion[0])
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if __name__ == "__main__":
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run()
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```
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## 基准测试性能
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**Satori-7B-Round2** 在领域内推理基准（数学推理）和领域外基准（一般推理任务）上进行了评估。所有结果均报告为零样本 pass@1 准确率，使用贪婪采样。
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### 评估任务
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- **数学推理基准**：GSM8K、MATH500、AMC2023、AIME2024 和 OlympiadBench。除了 GSM8K，所有其他数据集都包含竞赛级别的问题。
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- **一般领域推理基准**：
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- 逻辑推理：FOLIO、BoardgameQA（BGQA）。
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- 代码推理：CRUXEval。
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- 常识推理：StrategyQA（STGQA）。
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- 表格推理：TableBench。
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- 特定领域推理：MMLUPro STEM 子集（STEM），包括物理、化学、计算机科学、工程、生物学和经济学。
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### 数学推理基准
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**Satori-7B-Round2** 实现了 SOTA 性能，并且优于使用相同基础模型（Qwen-2.5-Math-7B）的 Qwen-2.5-Math-7B-Instruct。
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| 规模 | 模型 | GSM8K | MATH500 | OlymBench | AMC2023 | AIME2024 | 平均值 |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| **大型** | Llama-3.1-70B-Instruct | 94.1 | 68.0 | 29.4 | 42.5 | 13.3 | 49.5 |
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| | OpenMath2-Llama3.1-70B | 94.1 | 71.8 | 30.1 | 45.0 | 13.3 | 50.9 |
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| | QwQ-32B-Preview | 95.5 | 90.6 | 61.2 | 77.5 | 50.0 | 75.0 |
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| **小型** | Llama-3.1-8b-Instruct | 84.4 | 51.9 | 15.1 | 22.5 | 3.3 | 35.4 |
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| | OpenMath2-Llama3.1-8B | 90.5 | 67.8 | 28.9 | 37.5 | 6.7 | 46.3 |
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| | NuminaMath-7B-CoT | 78.9 | 54.6 | 15.9 | 20.0 | 10.0 | 35.9 |
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| | Qwen-2.5-7B-Instruct | 91.6 | 75.5 | 35.5 | 52.5 | 6.7 | 52.4 |
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| | Qwen-2.5-Math-7B-Instruct | 95.2 | 83.6 | 41.6 | 62.5 | 16.7 | 59.9 |
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| | **Satori-7B-Round2** | 93.9 | 83.6 | 48.5 | 72.5 | 23.3 | **64.4** |
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### 一般领域推理基准
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仅在数学数据集上训练的 **Satori-7B-Round2** 在多种领域外推理基准上表现出强大的迁移能力，并且大幅超越了 Qwen-2.5-Math-7B-Instruct。此外，尽管未在其他领域进行训练，**Satori-7B-Round2** 的性能与或超过了其他小型通用指令模型。
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| 规模 | 模型 | FOLIO | BGQA | CRUXEval | StrategyQA | TableBench | STEM | 平均值 |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| **大型** | Llama-3.1-70B-Instruct | 65.0 | 58.3 | 59.6 | 88.8 | 34.2 | 61.7 | 61.3 |
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| | OpenMath2-Llama3.1-70B | 68.5 | 68.7 | 35.1 | 95.6 | 46.8 | 15.1 | 55.0 |
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| | QwQ-32B-Preview | 84.2 | 71.1 | 65.2 | 88.2 | 51.5 | 71.3 | 71.9 |
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| **小型** | Llama-3.1-8b-Instruct | 63.5 | 50.3 | 38.5 | 92.2 | 32.4 | 43.4 | 53.4 |
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| | OpenMath2-Llama3.1-8B | 57.1 | 49.0 | 11.1 | 84.4 | 34.2 | 10.9 | 41.1 |
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| | NuminaMath-7B-CoT | 53.2 | 44.6 | 28.0 | 77.8 | 29.1 | 11.3 | 40.7 |
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| | Qwen-2.5-7B-Instruct | 72.4 | 53.0 | 58.1 | 91.3 | 43.2 | 57.1 | **62.5** |
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| | Qwen-2.5-Math-7B-Instruct | 68.9 | 51.3 | 28.0 | 85.3 | 36.2 | 45.2 | 52.5 |
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| | **Satori-7B-Round2** | 72.9 | 58.5 | 41.1 | 90.4 | 44.6 | 57.4 | **60.8** |
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## 资源
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我们提供了我们的训练数据集：
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- 包含 300K 个唯一问题的完整格式调整数据集。 [okwinds/Satori_FT_data](https://modelscope.cn/datasets/okwinds/Satori_FT_data)
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- 包含 550K 个唯一问题的 RL 数据集。 [okwinds/Satori_RL_data](https://modelscope.cn/datasets/okwinds/Satori_RL_data)
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## 引用
|
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如果您觉得我们的模型和数据有帮助，请引用我们的论文：
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||||
```
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@misc{shen2025satorireinforcementlearningchainofactionthought,
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title={Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search},
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author={Maohao Shen and Guangtao Zeng and Zhenting Qi and Zhang-Wei Hong and Zhenfang Chen and Wei Lu and Gregory Wornell and Subhro Das and David Cox and Chuang Gan},
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year={2025},
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eprint={2502.02508},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.02508},
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}
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```
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|explore|>": 151667,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|continue|>": 151665,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|mask|>": 151668,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|reflect|>": 151666,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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config.json
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config.json
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{
|
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"_name_or_path": "/proj/long-multi/zfchen/LLaMA-Factory/saves/ministrawburry_full/Qwen2.5_Math_type_I_II_III_New_SFT_iter2_lr2e-5_NuminaOMI_top3_180K_hard",
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"architectures": [
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|
||||
35
special_tokens_map.json
Normal file
35
special_tokens_map.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"<|continue|>",
|
||||
"<|reflect|>",
|
||||
"<|explore|>",
|
||||
"<|mask|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
757480
tokenizer.json
Normal file
757480
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
244
tokenizer_config.json
Normal file
244
tokenizer_config.json
Normal file
@@ -0,0 +1,244 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|continue|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151666": {
|
||||
"content": "<|reflect|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151667": {
|
||||
"content": "<|explore|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151668": {
|
||||
"content": "<|mask|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"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|>",
|
||||
"<|continue|>",
|
||||
"<|reflect|>",
|
||||
"<|explore|>",
|
||||
"<|mask|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"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": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"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