arXiv AI
Jun 2

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

arXiv:2606. 00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce uncertainty; without such retries, a greedy policy is optimal.

By Soichiro Nishimori, Paavo Parmas, Sotetsu Koyamada, Tadashi Kozuno, Toshinori Kitamura, Shin Ishii, Yutaka Matsuo
arXiv AI
Sep 2

ReNFT: Repairing Mode Collapse in Reward Post-Training via Internal Probability-Mass Recalibration

ReNFT is a method that repairs mode collapse in diffusion generators after reward post‑training by internally recalibrating probability mass. It identifies suppressed alternatives through anti‑hub prompts and uses two policy‑dominated routes to generate counterfactual proposals, then applies reward‑based ranking and a joint‑and‑paired NFT update to restore diversity while preserving reward. Experiments on PickScore and GenEval show that ReNFT retains almost all of the original reward while significantly boosting diversity metrics.

By Yuchen Bao, Chao Wen, Haowei Wang, Ruoxin Chen, Donghao Luo, Jiahui Zhan, Wenjian Huang, Shen Chen, Yiting Wang, Taiping Yao, Chengjie Wang, Shouhong Ding, Jianguo Zhang