In today's post, we delve into a recent paper that investigates the intricacies of Reinforcement Learning in the context of Large Language Models (LLMs). This study shines a light on the challenges and nuances of training such models to align better with human preferences. …
We've all been there - diligently using Proximal Policy Optimization (PPO) for text generation, only to wonder if there's more to be extracted from our models. If you've been in this boat, you're in for a treat! A recent paper under review for ICLR 2024 offers some intriguing insights. …
In the realm of reinforcement learning, Proximal Policy Optimization (PPO) stands out for its remarkable balance between sample efficiency, ease of use, and generalization. However, delving into PPO can sometimes lead you into a quagmire of NaNs and Infs, especially when dealing with complex environments. This post chronicles our journey through these challenges and sheds light on strategies that ensured stable and robust policy optimization. …