HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime

Mohamed Sana, Nicola Piovesan, Antonio De Domenico +2

We investigate a narrow but common failure mode of GRPO-style reinforcement learning in the context of sparse verifiable rewards: early updates contain more responses with negative…