…Trained primarily for short-horizon continuation, they treat long sequences as extended single shots, inevitably suffering from motion stagnation and semantic drift during long rollouts. To bridge this gap, we introduce CausalCine…
…those whose answers never appear in 128 pre-RL samples (only ~18% of training data) drive ~83% of the gain, since rare correct rollouts still emerge during training and get reinforced. Together…
…Starting from a pretrained VLA policy, LWD closes the loop between deployment, shared physical experience, policy improvement , and redeployment by using autonomous rollouts and human interventions collected across a robot fleet. To…
…To bridge this gap, we propose a unified framework that performs both within a single autoregressive rollout and optimizes them end-to-end via factorized group-relative policy optimization ( F-GRPO ). Our…
…A Native World Model Stack for Physical AI Published on Jun 16 Submitted by Shan You on Jun 18 #3 Paper of the day Authors: , , Shan You , , , , Zhisheng Zheng , , , Xiaoming Wu , , , , , , , , , , , Xiaoning…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI…
…Across AliasBench , SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines View arXiv page View PDF GitHub 9 Add to collection Community This is an automated message from…
…🧠 Agentic streaming inference. An agentic inference framework that supports thousand-second-scale generation while mitigating drift through agentic cache management, chunk commitment, long-context rollout, and prompt planning. 📊 SocialVideo-Bench. A…
…DiffusionOPD first trains task-specific teachers independently, then distills their capabilities into a unified student along the student own rollout trajectories. This decouples single-task exploration from multi-task integration and avoids…
…when all sampled rollouts for a query fail, the relative advantage collapses to zero. Consequently, the model loses effective training signals for these questions, wasting the training data and computational budget. While…