@inproceedings{arora-etal-2026-current,
    title = "Current Advances in {LLM} Reasoning",
    author = "Arora, Akhil  and
      Chaudhary, Vishrav  and
      Kreutzer, Julia  and
      Potamitis, Nearchos  and
      Dziri, Nouha  and
      Tandon, Niket",
    editor = "Andreas, Jacob  and
      Murray, Kenton",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Tutorial Abstracts)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-1.4/",
    pages = "7--8",
    ISBN = "979-8-89176-394-4",
    abstract = "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? What are the next frontiers in LLM reasoning? This tutorial answers these questions through a unified view of LLM reasoning. This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of models and discusses two types of methods to improve models' reasoning: advanced inference time methods, such as structured and self-improvement inference methods, and (ii) post-training methods, such as RLHF, DPO, and GRPO that aim to make LLMs think more like humans. The tutorial explores these technical discussions while maintaining a practical outlook through illustrative demos and short guided hands-on exercises. The tutorial is designed for both researchers and practitioners seeking practical insights into LLM reasoning."
}