MAdam preconditions MOO solver directions with preference-conditioned curvature so that Adam's adaptive steps respect the intended metric instead of entangling it with gradient history.
arXiv preprint arXiv:2308.12029 , year =
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MI-EPO maximizes joint conditional mutual information among responses, feedback, and preference vectors, using probabilistic routing to improve alignment and controllability in multi-objective LLM optimization.
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MAdam: Metric-Aware Multi-Objective Adam
MAdam preconditions MOO solver directions with preference-conditioned curvature so that Adam's adaptive steps respect the intended metric instead of entangling it with gradient history.