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Explore a comprehensive 32-minute video presentation examining the unified theory of agentic reasoning through a geometric lens, featuring cutting-edge research from UC Berkeley, NVIDIA, and other leading institutions. Delve into the mathematical foundations connecting Q-Learning, gradient policy reinforcement learning, and large reasoning models within a unified framework. Discover how researchers are mapping complex reasoning processes onto geometric manifolds and low-dimensional subspaces to better understand how AI systems perform sophisticated reasoning tasks. Learn about the GSM-AGENT framework for understanding agentic reasoning in controllable environments, the REMA unified reasoning manifold framework for interpreting large language models, and theoretical perspectives on reinforcement learning benefits and pitfalls in language model planning. Examine the implications for next-generation AI systems including GPT-5 and DeepSeek V3, while gaining insights into supervised fine-tuning approaches and agentic reasoning graphs that could shape the future of artificial intelligence reasoning capabilities.
