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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Latest Advances on System-2 Reasoning
| Date | Stars |
|---|---|
| 2026-07-24 | 1352 |
| 2026-07-25 | 1352 |
| 2026-07-28 | 1352 |
| 2026-07-30 | 1352 |
| 2026-07-31 | 1352 |
| 2026-08-06 | 1352 |
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growth rate 0.00%/day
# Awesome-System2-Reasoning-LLM [](http://arxiv.org/abs/2502.17419) [](https://github.com/zzli2022/System2-Reasoning-LLM) [](https://github.com/zzli2022/System2-Reasoning-LLM) []() <!-- omit in toc --> ## 📢 Updates - **2025.02**: We released a survey paper "[From System 1 to System 2: A Survey of Reasoning Large Language Models](http://arxiv.org/abs/2502.17419)". Feel free to cite or open pull requests. <!-- omit in toc --> ## 👀 Introduction Welcome to the repository for our survey paper, "From System 1 to System 2: A Survey of Reasoning Large Language Models". This repository provides resources and updates related to our research. For a detailed introduction, please refer to [our survey paper](http://arxiv.org/abs/2502.17419). Achieving human-level intelligence requires enhancing the transition from System 1 (fast, intuitive) to System 2 (slow, deliberate) reasoning. While foundational Large Language Models (LLMs) have made significant strides, they still fall short of human-like reasoning in complex tasks. Recent reasoning LLMs, like OpenAI’s o1, have demonstrated expert-level performance in domains such as mathematics and coding, resembling System 2 thinking. This survey explores the development of reasoning LLMs, their foundational technologies, benchmarks, and future directions. We maintain an up-to-date GitHub repository to track the latest developments in this rapidly evolving field.  This image highlights the progression of AI systems, emphasizing the shift from rapid, intuitive approaches to deliberate, reasoning-driven models. It shows how AI has evolved to handle a broader range of real-world challenges.  The recent timeline of reasoning LLMs, covering core methods and the release of open-source and closed-source reproduction projects. <!-- omit in toc --> ## 📒 Table of Contents - [Awesome-System-2-AI](#awesome-system-2-ai) - [Part 1: O1 Replication](#part-1-o1-replication) - [Part 2: Process Reward Models](#part-2-process-reward-models) - [Part 3: Reinforcement Learning](#part-3-reinforcement-learning) - [Part 4: MCTS/Tree Search](#part-4-mctstree-search) - [Part 5: Self-Training / Self-Improve](#part-5-self-training--self-improve) - [Part 6: Reflection](#part-6-reflection) - [Part 7: Efficient System2](#part-7-efficient-system2) - [Part 8: Explainability](#part-8-explainability) - [Part 9: Multimodal Agent related Slow-Fast System](#part-9-multimodal-agent-related-slow-fast-system) - [Part 10: Benchmark and Datasets](#part-10-benchmark-and-datasets) - [Part 11: Reasoning and Safety](#part-11-reasoning-and-safety) - [Part 12: R1 Driven Multimodal Reasoning Enhancement](#part-12-r1-driven-multimodal-reasoning-enhancement) ## Part 1: O1 Replication * O1 Replication Journey: A Strategic Progress Report -- Part 1 [[Paper]](https://arxiv.org/abs/2410.18982)  * Enhancing LLM Reasoning with Reward-guided Tree Search [[Paper]](https://arxiv.org/abs/2411.11694)  * Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions [[Paper]](https://arxiv.org/abs/2411.14405)  * O1 Replication Journey--Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson? [[Paper]](https://arxiv.org/abs/2411.16489)  * Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [[Paper]](https://arxiv.org/abs/2412.09413) 
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yueliu1999 · National University of Singapore · Singapore
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b1144f7df60ebc7c, topic:rl, readme:reinforcement learning
matched fp:b1144f7df60ebc7c, topic:benchmark