Author

Soichiro Nishimori

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#artificial intelligence May 2026

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

ReMax PPO (RePPO) is introduced, a PPO variant that optimizes ReMax while generalizing the discrete retry count $M$ to a continuous parameter $m>0$, enabling fine-grained control of exploration.

Soichiro Nishimori, Paavo Parmas, Sotetsu Koyamada et al. · arXiv.org · 7 citations · ⚡1

#machine learning Jul 2026

Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation

This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.

Ku Onoda, Paavo Parmas, Hiroki Furuta et al. · arXiv.org · 3 citations