In this episode, we talk with Will Brown, a research lead at Prime Intellect, about his journey into reinforcement learning (RL) and multi-agent systems, exploring their theoretical foundations and practical applications. We discuss the importance of RL in the current LLMs pipeline and the challenges it faces. We also discuss applying agentic workflows to real-world applications and the ongoing evolution of AI development.Chapters00:00 Introduction to Reinforcement Learning and Will's Journey03:10 Theoretical Foundations of Multi-Agent Systems06:09 Transitioning from Theory to Practical Applications09:01 The Role of Game Theory in AI11:55 Exploring the Complexity of Games and AI14:56 Optimization Techniques in Reinforcement Learning17:58 The Evolution of RL in LLMs21:04 Challenges and Opportunities in RL for LLMs23:56 Key Components for Successful RL Implementation27:00 Future Directions in Reinforcement Learning36:29 Exploring Agentic Reinforcement Learning Paradigms38:45 The Role of Intermediate Results in RL41:16 Multi-Agent Systems: Challenges and Opportunities45:08 Distributed Environments and Decentralized RL49:31 Prompt Optimization Techniques in RL52:25 Statistical Rigor in Evaluations55:49 Future Directions in Reinforcement Learning59:50 Task-Specific Models vs. General Models01:02:04 Insights on Random Verifiers and Learning Dynamics01:04:39 Real-World Applications of RL and Evaluation Challenges01:05:58 Prime RL Framework: Goals and Trade-offs01:10:38 Open Source vs. Closed Source Models01:13:08 Continuous Learning and Knowledge ImprovementMusic:"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0."Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.Changes: trimmed

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