New AI Post-Training - Add RL as Orthogonal Vector to SFT

Explore a groundbreaking approach to AI post-training that combines reinforcement learning (RL) with supervised fine-tuning (SFT) as orthogonal vectors. Delve into the research paper "Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation" by researchers from Peking University's Institute for Artificial Intelligence and BIGAI. Learn how this innovative methodology addresses the limitations of traditional knowledge-based training by incorporating RL skills that enable continuous adaptation in AI systems. Discover the theoretical foundations and practical implications of treating RL and SFT as orthogonal components in the training process, and understand how this approach can enhance AI model performance and adaptability. Examine the research findings that demonstrate why knowledge alone is insufficient for optimal AI performance and how reinforcement learning skills can be systematically integrated to create more robust and adaptive AI systems.