Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. However, reward-maximizing RL causes policy mode collapse even under reference KL or entropy regularization, reducing the policy to a single high-reward mode. In T2I, thi...
Zhi-Yuan Ma, Jia-Ming Li, Ling-Zhen Li et al.· 0 citations
Experiments demonstrate strong 3D perception and driving scene understanding while largely preserving general vision-language capability and comprehensive evaluations across open-loop, pseudo-closed-loop, and closed-loop settings further show highly competitive motion-planning performance.
Xin Zhou, Zongchuang Zhao, Zhibo Yang et al.· 9 citations· ⚡2
TurboVLA is introduced, a new VLA paradigm that reformulates the conventional LLM-centric pathway as a direct VLA mapping, and constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference.