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This work proposes RosePO, a framework to refine LLM-based recommendation through pairwise preference optimization with personalized smoothing, and incorporates a personalized smoothing factor predicted by a user oracle into the optimization objective.
This work proposes ARMOR (Anchor Rollout and Mixed Optimization for RL), a framework that shifts the paradigm from passive penalty to active sample stabilization, enabling sustained performance improvements over extended training horizons.
Perception-Enhanced Alignment DPO (PEA-DPO), a framework for multimodal LLMs alignment, which explicitly leverages visual preference signals to overcome visual insensitivity is proposed, which demonstrates that PEA-DPO enhances sensitivity to visual context while preserving the language modeling capacity of the base model.
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