🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

🗓️ 17 Oct 2025
📚 AI News & Trends

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...

#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization

🤖🧠 The Art of Scaling Reinforcement Learning Compute for LLMs: Top Insights from Meta, UT Austin and Harvard University

🗓️ 21 Oct 2025
📚 AI News & Trends

As Large Language Models (LLMs) continue to redefine artificial intelligence, a new research breakthrough has emerged from Meta, The University of Texas at Austin, University College London, UC Berkeley, Harvard University and Periodic Labs. Their paper, titled “The Art of Scaling Reinforcement Learning Compute for LLMs,” introduces a transformative framework for understanding how reinforcement learning ...

#ReinforcementLearning #LLMs #AIResearch #Meta #UTAustin #HarvardUniversity