New Inverse Probability Scaling method tackles mode collapse in reinforcement learning

A new research paper proposes a method called Inverse Probability Scaling (IPS) to address outcome-level mode collapse in reinforcement learning (RL). The paper argues that standard expected return maximization objectives inherently lead to policies collapsing onto a limited set of outcomes, regardless of exploration strategies or regularization. IPS aims to correct this by removing outcome-frequency amplification from the learning signal, which theoretically ensures a more diverse distribution of high-quality outcomes. The researchers implemented this as IPS-GRPO, a modification to Group Relative Policy Optimization, and demonstrated its effectiveness in reducing mode collapse on reasoning and molecular generation tasks. AI

IMPACT Addresses a fundamental limitation in reinforcement learning, potentially enabling more robust training for diverse outcome generation.

RANK_REASON Research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Abhijeet S Sinha
• Group Relative Policy Optimization
• Hugging Face
• Inverse Probability Scaling
• IPS-GRPO
• reinforcement learning

• paper
• other

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