AI RESEARCH
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
arXiv CS.CV
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ArXi:2604.15308v1 Announce Type: new High-level autonomous driving requires motion planners capable of modeling multimodal future uncertainties while remaining robust in closed-loop interactions. Although diffusion-based planners are effective at modeling complex trajectory distributions, they often suffer from stochastic instabilities and the lack of corrective negative feedback when trained purely with imitation learning. To address these issues, we propose RAD-2, a unified generator-discriminator framework for closed-loop planning.