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Awesome Long Chain-of-Thought Reasoning

Added Feb 23, 2026
Docs, Tutorials & Resources
Open Source
PythonPyTorchLarge Language ModelsKnowledge BaseTransformersAI AgentsNatural Language ProcessingDocs, Tutorials & ResourcesOtherEducation & Research ResourcesModel Training & Inference

A curated collection of resources for Long Chain-of-Thought (Long-CoT) reasoning in LLMs, featuring papers, implementations, and datasets to track the latest advancements in the field.

Awesome Long Chain-of-Thought Reasoning#

Overview#

A curated list of resources dedicated to Long Chain-of-Thought (Long-CoT) Reasoning. Following the release of models like OpenAI o1 and DeepSeek-R1, "Long Chain-of-Thought"—performing deep, multi-step internal reasoning before responding—has become a key technique for enhancing logical capabilities in Large Language Models.

This project systematically organizes research achievements in the Long-CoT field within AI, addressing the information fragmentation caused by the explosive growth of research in this area.

Core Value#

  • Academic Research Portal: Quick access to SOTA papers and theoretical foundations in Long-CoT
  • Development Reference: Index of open-source reasoning chain datasets and RL fine-tuning code
  • Technology Trend Tracking: Understanding mainstream long-reasoning model architectures and paradigm evolution

Resource Categories#

CategoryDescription
Paper CollectionArXiv academic paper index related to Long-CoT
Open-source ImplementationsRelated GitHub repos (e.g., DeepSeek-R1, Open-O1)
DatasetsData resources for training long-reasoning capabilities
MethodologiesSub-directions including Tree of Thoughts, Self-Correction, RL Fine-tuning

Use Cases#

  • LLM Reasoning Research
  • Prompt Engineering & System 2 Thinking exploration
  • RLHF/RLAIF-related Reinforcement Learning applications
  • Multi-modal long-reasoning tasks

Quick Start#

# Clone for local browsing
git clone https://github.com/LightChen233/Awesome-Long-Chain-of-Thought-Reasoning.git

Or visit the GitHub repository directly, browse the categorized table of contents in the README, and navigate to resource links of interest.

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