开源项目教程：Awesome-LLM-Reasoning-Openai-o1-Survey

开源项目教程：Awesome-LLM-Reasoning-Openai-o1-Survey

Awesome-LLM-Reasoning-Openai-o1-Survey The related works and background techniques about Openai o1 项目地址: https://gitcode.com/gh_mirrors/aw/Awesome-LLM-Reasoning-Openai-o1-Survey

1. 项目目录结构及介绍

Awesome-LLM-Reasoning-Openai-o1-Survey 项目是一个关于 OpenAI o1 相关研究的资料汇总，它包含了大量的文献调研和开源论文。以下是项目的目录结构：

.
├── LICENSE
├── README.md
├── papers
│   ├── Complex_Logical_Reasoning
│   ├── Generative_Language_Modeling_for_Automated_Theorem_Proving
│   ├── Hypothesis_Search_Inductive_Reasoning_with_Language_Models
│   ├── Phenomenal_Yet_Puzzling_Testing_Inductive_Reasoning_Capabilities_of_Language_Models_with_Hypothesis_Refinement
│   ├── Training_Verifiers_to_Solve_Math_Word_Problems
│   ├── To_CoT_or_not_to_CoT_Chain-of-thought_Helps_Mainly_on_Math_and_Symbolic_Reasoning
│   ├── STaR_Self-Taught_Reasoner_Bootstrapping_Reasoning_With_Reasoning
│   ├── Quiet-STaR_Language_Models_Can_Teach_Themselves_to_Think_Before_Speaking
│   ├── Training_Chain-of-thought_via_Latent-variable_Inference
│   ├── Chain-of-thought_Reasoning_without_Prompting
│   ├── Mutual_Reasoning_Makes_Smaller_LLMs_Stronger_Problem-Solvers
│   ├── Large_Language_Monkeys_Scaling_Inference_Compute_with_Repeated_Sampling
│   ├── Scaling_LLM_Test-Time_Compute_Optimally_Can_be_More_Effective_than_Scaling_Model_Parameters
│   ├── Training_Language_Models_to_Self-Correct_via_Reinforcement_Learning
│   ├── From_Medprompt_to_o1_Exploration_of_Run-Time_Strategies_for_Medical_Challenge_Problems_and_Beyond
│   ├── Self-play_Learning
│   ├── Language_Models_Can_Teach_Themselves_to_Program_Better
│   ├── Large_Language_Models_Can_Self-Improve
│   ├── Self-Play_Fine-Tuning_Converts_Weak_Language_Models_to_Strong_Language_Models
│   ├── Self-Play_Preference_Optimization_for_Language_Model_Alignment
│   ├── Scalable_Online_Planning_via_Reinforcement_Learning_Fine-Tuning
│   ├── Generative_Verifiers_Reward_Modeling_as_Next-Token_Prediction
│   ├── Accessing_GPT-4_level_Mathematical_Olympiad_Solutions_via_Monte_Carlo_Tree_Self-refine_with_LLaMa-3_8B
│   ├── Interpretable_Contrastive_Monte_Carlo_Tree_Search_Reasoning
│   ├── Solving_Math_Word_Problems_with_Process-and_Outcome-based_Feedback
│   ├── Thinking_Fast_and_Slow_With_Deep_Learning_and_Tree_Search
│   ├── Let’s_Verify_Step_by_Step
│   ├── OVM_Outcome-supervised_Value_Models_for_Planning_in_Mathematical_Reasoning
│   ├── LLM_Critics_Help_Catch_LLM_Bugs
│   ├── Self-critiquing_Models_for_Assisting_Human_Evaluators
│   ├── Improve_Mathematical_Reasoning_in_Language_Models_by_Automated_Process_Supervision
│   └── Q*_Improving_Multi-step_Reasoning_for_LLMs_with_Deliberative_Planning

每个子目录下包含了相关论文的链接和简要介绍。

2. 项目的启动文件介绍

该项目的启动主要是通过查看 README.md 文件来了解项目的基本信息和目的。README.md 文件通常包含以下内容：

• 项目的背景和目的
• 项目包含的资料和文献
• 如何浏览和使用这些资料
• 贡献者信息和项目的维护状态

用户可以通过标准的 Markdown 文件查看器打开 README.md 文件来启动对项目的了解。

3. 项目的配置文件介绍

在这个项目中，并没有特定的配置文件。因为该项目主要是作为一个资料库，用户不需要进行特定的配置。如果用户需要将项目集成到自己的开发环境中，可能需要根据自己的需求创建配置文件。通常，这类配置文件可能包括：

• 数据存储配置
• API密钥或其他认证信息
• 环境变量设置

由于本项目是一个静态的资料库，因此不需要复杂的配置文件。

Awesome-LLM-Reasoning-Openai-o1-Survey The related works and background techniques about Openai o1 项目地址: https://gitcode.com/gh_mirrors/aw/Awesome-LLM-Reasoning-Openai-o1-Survey