New Paradigm: AI Research Summaries

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

This episode analyzes the research paper "WHAT MATTERS IN TRANSFORMERS? NOT ALL ATTENTION IS NEEDED," authored by Shwai He, Guoheng Sun, Zhenyu Shen, and Ang Li from the University of Maryland, College Park, and released on October 17, 2024. The discussion explores the inefficiencies within Transformer-based large language models, specifically examining the redundancy in Attention layers, Blocks, and MLP layers. Using a similarity-based metric, the study reveals that many Attention layers contribute minimally to model performance, enabling significant pruning without substantial loss in accuracy. For instance, pruning half of the Attention layers in the Llama-2-70B model achieved a 48.4% speedup with only a 2.4% performance decline.Additionally, the episode reviews the "Joint Layer Drop" method, which combines the pruning of both Attention and MLP layers, allowing for more aggressive reductions while maintaining performance integrity. Applied to the Llama-2-13B model, this approach preserved 90% of its performance on the MMLU task despite dropping 31 layers. The research underscores the potential for developing more efficient and scalable AI models by optimizing Transformer architectures, challenging the notion that larger models are always better and paving the way for sustainable advancements in artificial intelligence.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://arxiv.org/pdf/2406.15786

This episode analyzes the research paper "Cultural Evolution of Cooperation among LLM Agents" by Aron Vallinder and Edward Hughes, affiliated with Independent and Google DeepMind. It explores how large language model agents develop cooperative behaviors through interactions modeled by the Donor Game, a classic economic experiment that assesses indirect reciprocity. The analysis highlights significant differences in cooperation levels among models such as Claude 3.5 Sonnet, Gemini 1.5 Flash, and GPT-4o, with Claude 3.5 Sonnet demonstrating superior performance through mechanisms like costly punishment to enforce social norms. The episode also examines the influence of initial conditions on the evolution of cooperation and the varying degrees of strategic sophistication across different models. Furthermore, the discussion delves into the implications of these findings for the deployment of AI agents in society, emphasizing the necessity of carefully designing and selecting models that can sustain cooperative infrastructures. The researchers propose an evaluation framework as a new benchmark for assessing multi-agent interactions among large language models, underscoring its importance for ensuring that AI integration contributes positively to collective well-being. Overall, the episode underscores the critical role of cooperative norms in the future of AI and the nuanced pathways required to achieve them.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://www.arxiv.org/pdf/2412.10270

This episode analyzes the research paper titled **"TEMPERA: Test-Time Prompt Editing via Reinforcement Learning,"** authored by Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E. Gonzalez from UC Berkeley, Google Research, and the University of Alberta. The discussion centers on TEMPERA's innovative approach to optimizing prompts for large language models, particularly in zero-shot and few-shot learning scenarios. By leveraging reinforcement learning, TEMPERA dynamically adjusts prompts in real-time based on individual queries, enhancing efficiency and adaptability compared to traditional prompt engineering methods.The episode delves into the key features and performance of TEMPERA, highlighting its ability to utilize prior knowledge effectively while maintaining high adaptability through a novel action space design. It reviews the substantial performance improvements TEMPERA achieved over state-of-the-art techniques across various natural language processing tasks, such as sentiment analysis and topic classification. Additionally, the analysis covers TEMPERA's superior sample efficiency and robustness demonstrated through extensive experiments on multiple datasets. The episode underscores the significance of TEMPERA in advancing prompt engineering, offering more intelligent and responsive AI solutions.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://arxiv.org/pdf/2211.11890