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This paper introduces Spectral Souping, a framework for efficiently aligning LLMs with individual user preferences by discovering a universal spectral representation that enables merging of specialized policies at inference time without costly retraining.
This paper introduces Implicit Preference Alignment (IPA), a data-efficient post-training framework that improves hand motion generation in human image animation without requiring paired preference data. It utilizes implicit reward maximization and hand-aware local optimization to enhance generation quality while reducing data curation costs.