APO propose d’apprendre à plusieurs des initialisations pour aligner les LLM sur des préférences individuelles

Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferences, but scarce user-specific feedback makes personalized training difficult.