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While reinforcement learning with verifiable rewards provides reliable outcome supervision for VideoLLMs, sequence-level rewards offer limited token-level guidance. On-policy self-distillation addresses this limitation by conditioning a self-teacher on privileged information to provide dense token-level supervision. Ho...
GeoAnchor decomposes 3D spatial information into three complementary components: position latents for object grounding, direction latents for relational orientation, and geometry latents for scene structure, and is introduced a collaborative training strategy that guides the model from local spatial perception to compr...
This work introduces Spatially-Grounded Thought Tokenization to explicitly align special tokens with spatial locations for clear spatial correspondence and visual interpretability, and proposes Mixture-of-Thought-Tokens, a new free-form multimodal grounding method that bridges the perception-reasoning gap.
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