multi-objective-alignment

Self-OPD introduces a teacher-free on-policy distillation framework for flow matching models that uses self-explored stochastic branches and normalized advantages to optimize velocity fields, outperforming prior methods in multi-objective alignment.

Proposes MI-EPO, an information-theoretic framework for multi-objective alignment of large language models that uses mutual information to enhance exploration and ensure generated responses are distinguishable and aligned with different preference vectors, achieving stable trade-offs across conflicting objectives.