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  1. 1

    Video Generation Models: A Survey of Post-Training and Alignment

    This survey reviews post-training and alignment strategies for video generation models, which often struggle with human intent and temporal coherence despite strong pretraining. It proposes a new taxonomy, categorizing methods into supervised fine-tuning, self-training, preference-based, and inference-time approaches.

    Hugging Face Daily Papersarxiv.org1 minpaper
  2. 2

    In-Context Learning for Robots: Methods and Applications

    This literature review surveys In-Context Learning (ICL) methods for robots, which enable general-purpose robots to infer new task requirements from demonstrations and interaction. It categorizes ICL into four families based on how contextual evidence connects to execution, clarifying their transfer assumptions and roles of training, correspondence, and memory.

    Hugging Face Daily Papersarxiv.org1 minpaper