Paper Proposes Mode-Conditioned Reinforcement Learning to Counter LLM Mode Collapse

A new arXiv preprint describes a reinforcement learning approach that conditions alignment training on output modes, aiming to keep language models diverse instead of collapsing onto a narrow set of responses. The authors argue that standard alignment training progressively reduces output variety, which hurts tasks needing open-ended exploration. The method is framed as a quality-diversity alignment technique addressing that trade-off.