Understanding Diversity Collapse in RLVR via the Lens of Overtraining
A new research paper published on arXiv explores the phenomenon of "diversity collapse" in Reinforcement Learning with Verifiable Rewards (RLVR), a technique used to enhance large language models' reasoning. The paper frames this issue as a form of overtraining, where models focus too much on already solved problems, leading to a degradation in high-k Pass@k metrics. The researchers propose a new method called Bayesian Boundary Gating (BBG) to mitigate this by directing optimization away from overtrained problems, showing improvements in reasoning benchmarks. AI
IMPACT This research offers a new perspective on improving LLM reasoning by addressing overtraining in RLVR, potentially leading to more robust and diverse model capabilities.