Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance

ArXi:2605.30056v1 Announce Type: cross Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approaches, one representative branch focuses on the sampling-based policy optimization. This design enables better exploration capability of the diffusion model, particularly at the beginning of