Current Advances in LLM Reasoning

A unified, hands-on tour of how well LLMs reason, how to make them reason better, and where the field is heading next. Presented as a half-day tutorial at three venues in 2026.

Overview

As Large Language Models (LLMs) increasingly tackle reasoning-heavy tasks, from mathematics to commonsense to multilingual understanding, researchers face three pressing questions: How well do models reason? How can we make them reason better? And what are the next frontiers in LLM reasoning?

This tutorial answers these questions through a unified view of LLM reasoning. We explore comprehensive evaluation strategies to assess the reasoning abilities of models and discuss two families of methods that improve reasoning: advanced inference-time methods and post-training methods.

The tutorial is designed for both researchers and practitioners seeking actionable insight into LLM reasoning.

Venues

The tutorial runs at three venues in 2026. Pages for ICML and ACL are below; the IJCAI page is coming soon.

Q&A & Discussion

Have a question about the tutorial, the slides, or the hands-on materials? Want to share ideas or connect with others working on LLM reasoning? Join the conversation on GitHub Discussions.