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TurnWise is proposed, a turn-level GRPO framework that aligns learning with individual streaming decisions and addresses the mismatch between trajectory-level feedback and turn-level interaction, promoting informative responses and reducing redundancy in streaming video understanding.
Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lack iterative feedback, while existing recursive improvement approaches often rely on self-generated experiences, limiting...
The results support OPD as preparation for RL and favor forward KL when OPD is followed by RL in the authors' comparison, which highlights the importance of both objective choice and the states receiving supervision for subsequent RL.
This work compares the student view and training fixed within each scale against three abstractions compiled offline, a named strategy, a method-independent framing, and a problem category, and against an answer-only control that keeps the destination but removes the path.
This work proposes a streaming recognition and gated LoRA framework for intrinsic VLM safety, which is trained from unsafe prefixes, transition statements, and safe continuations, so that it learns to redirect unsafe generations back to safe responses after activation.
This work introduces Cross-Video Scene Procedure Planning (CVSPP): given an answer-redacted start-goal query and K candidate videos, a model must retrieve the supporting video, localize the relevant window, and predict the action sequence.
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