A. Sinha

MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces

Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-quality solutions, a failure known as mode collapse. Inverse Probability Scaling (IPS) counters this in group-based policy gradients such as GRPO by dividing each reward...