Overcoming Performance Plateaus in Large Language Model Training with Reinforcement Learning
Large language models (LLMs) rely on training methods that help them improve their language understanding and generation. Reinforcement learning from verifiable rewards (RLVR) is one such approach, using reliable feedback signals to guide the model’s development. TL;DR The article reports that LLM training with RLVR can encounter performance plateaus where progress stalls. Prolonged Reinforcement Learning (ProRL) extends training steps to help overcome these plateaus, though challenges remain as models scale. Scaling rollouts increases the range of training experiences, potentially improving model learning and mimicking human trial-and-error learning. Understanding Performance Plateaus in LLM Training Performance plateaus occur when a model’s improvement slows or stops despite ongoing training. This can restrict the model’s ability to generate more accurate or natural language responses, posing difficulties for developers aiming to enhance LLM cap...
