Home Projects LM-reasoning
LM-reasoning
artificial-intelligence

LM-reasoning

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

by jeffhj · GitHub
Stars
Forks
License
Created
Last commit
artificial-intelligenceawesome-listchain-of-thoughtMIT
View on GitHub
In plain words

Explore a collection of research papers and resources focused on how large AI models can reason and think logically.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
LM-reasoning — GitHub preview card
📈 Star history
572570
2026-07-202026-08-31
📈 Track LM-reasoning

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

LM-reasoning has 570 stars on GitHub. It has been forked 37 times. It has been in active development since 2022. LM-reasoning is available under the MIT license. Its main topics are artificial-intelligence, awesome-list, chain-of-thought, chatgpt.

Frequently asked questions

What is LM-reasoning?

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

Is LM-reasoning open source?

LM-reasoning is an open-source project. It is released under the MIT license.

Is LM-reasoning free?

Yes. LM-reasoning is free and open source — you can use, modify and self-host it.

What license does LM-reasoning use?

LM-reasoning is available under the MIT license.

🏅 Maintainer of this project?
olud.ai badge — LM-reasoning

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=jeffhj-lm-reasoning)](https://olud.ai/project/jeffhj-lm-reasoning.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.