arXiv AI October 28, 2026 By Saeed Hedayatian, Harsh Shah, Celine Mneimneh, Stefanos Nikolaidis

Repulsive Population Policy Gradients for Behaviorally Diverse Reinforcement Learning

The paper introduces RePPO, a method that adapts Proximal Policy Optimization to jointly optimize a compact population of diverse, high‑quality policies using a population‑dependent, dynamically shaped reward. RePPO achieves a strong quality‑diversity tradeoff on standard continuous‑control tasks and scales to larger policy and behavior spaces, maintaining quality while significantly increasing diversity in pixel‑based tasks. The authors also demonstrate RePPO’s applicability to fine‑tuning large language models, showing for the first time that quality‑diversity reinforcement learning can scale to this domain.

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