atfortes/Awesome-LLM-Reasoning

From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓

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This collection helps AI researchers and practitioners explore how to improve the reasoning abilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). It compiles academic papers and resources on techniques like Chain-of-Thought prompting, analysis of reasoning performance, and methods for scaling LLMs. Researchers, data scientists, and AI engineers working on advanced LLM applications would use this to understand the current state-of-the-art and challenges in making LLMs 'think' more effectively.

3,558 stars. No commits in the last 6 months.

Use this if you are actively researching, developing, or implementing advanced LLM applications and need a curated overview of techniques and analyses related to improving LLM reasoning capabilities.

Not ideal if you are looking for an introductory guide to using LLMs for basic tasks or seeking ready-to-use LLM tools without diving into the underlying research.

AI Research Natural Language Processing Machine Learning Engineering LLM Development Cognitive AI
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 18 / 25

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MIT

Last pushed

May 07, 2025

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