Overcoming Performance Plateaus in Large Language Model Training with Reinforcement Learning
Introduction to Reinforcement Learning in Language Models Large language models (LLMs) are advanced computer programs designed to understand and generate human language. Training these models requires methods that help them improve their ability to respond accurately and naturally. One such method is reinforcement learning from verifiable rewards (RLVR). This approach uses feedback signals that can be checked and trusted to guide the model's learning process. Challenges in LLM Training: Performance Plateaus When training LLMs with RLVR, a common problem is the appearance of performance plateaus. This means that after a certain point, the model stops improving even if training continues. These plateaus limit the model's ability to become better at understanding and generating language, which is a concern for researchers and developers. Previous Approaches: Prolonged Reinforcement Learning (ProRL) One method to address these plateaus is Prolonged Reinforcement Learning...