Online Learning Challenges
Online learning remains a complex challenge, with naive solutions often falling short. The discussion highlights the differences between online on-policy and off-policy paradigms in reinforcement learning, particularly in the context of fine-tuning large language models. Surprisingly, findings suggest that off-policy methods may not converge as slowly as traditionally thought, reshaping our understanding of sample efficiency in training LLMs.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - 680
Related Questions
What challenges are faced in training large language models (LLMs)?
How are Large Language Models (LLMs) fine-tuned post-training in the episode Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - 680 and the clip Exploration and Diversity?
How are large language models (LLMs) trained?