Beyond Reward Scoring: Online Multi-Agent Preference Learning for Multimodal Response Generation

arXiv:2610.23256v1 Announce Type: new Abstract: Multimodal preference alignment often follows a score-based RLHF pipeline: a reward model is first trained to learn human preferences, then used to score on-policy responses and optimize the response model's generation. This turns preference supervision into a scalar signal for generation: a model may learn to produce answers with higher preference scores, but is rarely trained explicitly to learn the reasons underlying human preferences. We therefore ask whether making preference understanding an internal capability of the response model can imp

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