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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.
A curated collection of LLM reasoning and planning resources, including key papers, limitations, benchmarks, and additional learning materials.
| Date | Stars |
|---|---|
| 2026-08-03 | 321 |
| 2026-08-12 | 322 |
| 2026-08-31 | 323 |
| 2026-09-13 | 324 |
| 2026-09-14 | 324 |
| 2026-10-30 | 325 |
| 2026-11-04 | 325 |
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<details open="open"> <summary>Topics covered</summary> - [About](#about) - [Overview](#overview) - [Techniques](#techniques) - [Reasoning Limitations](#reasoning-limitations) - [Benchmarks](#benchmarks) - [Miscellaneous](#miscellaneous) - [Additional Resources](#additional-resources) - [Acknowledgement](#acknowledgement) - [Contributing](#contributing) - [Authors \& contributors](#authors--contributors) </details> --- ## Announcements 02/27/2025 - Our papers on improving LLM Reasoning and Planning have been released - https://arxiv.org/abs/2502.12521, and https://arxiv.org/abs/2502.19295. ## About Welcome to the **Awesome LLMs Planning Reasoning** repository! This collection is dedicated to exploring the rapidly evolving field of Large Language Models (LLMs) and their capabilities in planning and reasoning. ## Overview As LLMs continue to demonstrate remarkable success in Natural Language Understanding (NLU) and Natural Language Generation (NLG), researchers are increasingly interested in assessing their abilities beyond traditional NLP tasks. One of the most promising and challenging areas of study is understanding how well LLMs can perform tasks that require planning and reasoning. These capabilities are essential for leveraging LLMs in more complex, real-world scenarios, such as autonomous decision-making, problem-solving, and strategic thinking. However, recent research suggests that LLMs often struggle with reasoning tasks that are relatively simple for most humans, highlighting the limitations of these models in this critical area. This repository is a curated list of research papers, code repositories, and benchmarks that focus on the intersection of LLMs with planning and reasoning tasks. Here, you'll find: - **Techniques**: Innovative methods that enable LLMs to reason and plan effectively, such as Chain-of-Thought prompting and Tree of Thoughts. - **Reasoning Limitations**: Critical investigations that explore the limitations and challenges LLMs face in planning and reasoning tasks. - **Benchmarks**: Standardized tests and evaluations designed to measure the performance of LLMs in these complex tasks. - **Miscellaneous Papers**: Papers related to the field of LLMs and reasoning, but not directly focused on planning tasks. - **Additional Resources**: Supplementary materials such as slides, dissertations, and other resources that provide further insights into LLM planning and reasoning. Whether you're a researcher, developer, or enthusiast, this repository serves as a comprehensive resource for staying updated on the latest advancements and understanding the current challenges in the domain of LLMs' planning and reasoning abilities. Dive in and explore the fascinating world where language models meet high-level cognitive tasks! </td></tr></table> ## Techniques | Paper | Link | Code | Venue | Date | Other | |-------|------|------|-------|------|-------| | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | [arXiv](https://arxiv.org/pdf/2201.11903) | -- | NeurIPS 22 | 28 Jan 2022 | [Video](https://www.youtube.com/watch?v=NBIRzSGHFro) | | Self-Consistency Improves Chain of Thought Reasoning in Language Models | [arXiv](https://arxiv.org/pdf/2203.11171) | -- | ICLR 23 | 7 Mar 2023 | [Video](https://www.youtube.com/watch?v=XKY2ThptBWs) | | REACT: Synergizing Reasoning and Acting in Language Models | [arXiv](https://arxiv.org/pdf/2210.03629) | [GitHub](https://github.com/ysymyth/ReAct) | ICLR 23 | 10 Mar 2023 | [Project](https://react-lm.github.io/) [Video](https://www.youtube.com/watch?v=QX-p-vsDoiQ) | | LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models | [arXiv](https://arxiv.org/pdf/2212.04088) | [GitHub](https://github.com/OSU-NLP-Group/LLM-Planner/) | ICCV 23 | 30 Mar 2023 | [Project](https://dki-lab.github.io/LLM-Planner/) | | Least-To-Most Prompting Enables Complex Reasoning In Large Language Models | [arXiv](https://arxiv.org/pdf/22
Excerpt of 19,566 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5e6070ad93e38916, llm:Description: 'A curated collection of LLM reasoning and planning resources, including key papers, limitations, benchmarks, and additional learning materials.' (awesome list of resources on LLM reasoning and planning).
matched fp:5e6070ad93e38916, llm:Description: 'A curated collection of LLM reasoning and planning resources, including key papers, limitations, benchmarks, and additional learning materials.' (awesome list of resources on LLM reasoning and planning).
matched fp:5e6070ad93e38916, llm:Description: 'A curated collection of LLM reasoning and planning resources, including key papers, limitations, benchmarks, and additional learning materials.' (awesome list of resources on LLM reasoning and planning).