This episode analyzes the research paper titled **"Mastering Board Games by External and Internal Planning with Language Models"**, authored by John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada Lewis, Anian Ruoss, Tom Zahavy, Petar Veličković, Laurel Prince, Satinder Singh, Eric Malmi, and Nenad Tomašev** from Google DeepMind, Google, and ETH Zürich. The study investigates the enhancement of large language models in multi-step planning and reasoning within complex board games such as Chess, Fischer Random Chess, Connect Four, and Hex.The researchers introduce two planning approaches—**external search** and **internal search**—to improve the strategic depth and decision-making capabilities of language models. By integrating search-based planning with pre-trained language models, the study achieves significant performance improvements, including Grandmaster-level proficiency in Chess with a comparable search budget to human players. The findings highlight the potential for these methodologies to extend beyond board games, suggesting applications in various fields that require nuanced decision-making and long-term planning.This podcast is created with the assistance of AI, the producers and editors take every effort to ensure each episode is of the highest quality and accuracy.For more information on content and research relating to this episode please see: https://storage.googleapis.com/deepmind-media/papers/SchultzAdamek24Mastering/SchultzAdamek24Mastering.pdf

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