This repository is a curated collection of research papers, blogs, talks, and open-source projects focused on Large Language Model (LLM) reasoning capabilities, particularly those inspired by OpenAI's "o1" models. It serves as a valuable resource for researchers and developers aiming to understand, replicate, or advance LLM reasoning techniques.

How It Works

The collection highlights advancements in LLM reasoning through various approaches, including Reinforcement Learning from Human Feedback (RLHF), self-play, chain-of-thought prompting, and process-based supervision. It emphasizes techniques that incentivize or guide LLMs to exhibit more robust reasoning, planning, and problem-solving skills, often drawing parallels to human cognitive processes.

Quick Start & Requirements

This repository is a curated list and does not have direct installation or execution commands. Users are directed to individual linked projects for specific setup instructions.

Highlighted Details

• Extensive coverage of OpenAI's "o1" models and related research.
• Links to numerous open-source implementations and replication efforts.
• Categorization of resources by type: papers, blogs, talks, courses, and Twitter discussions.
• Includes technical reports and benchmarks for various reasoning models.

Maintenance & Community

The repository is maintained by "hijkzzz" and is continuously updated to track the frontier of LLM reasoning research. Links to OpenAI developer forums and relevant Twitter discussions are provided for community engagement.

Licensing & Compatibility

The repository itself is a collection of links and does not impose a specific license. Individual linked projects will have their own licenses, which users must consult for compatibility and usage restrictions.

Limitations & Caveats

This is a curated list of external resources; it does not provide code for direct execution or replication. Users must navigate to individual project links for implementation details and potential limitations.