GGRO monitors token entropy to trigger gradient-guided token injection from reward models, improving LLM alignment on safety, helpfulness, and reasoning tasks at inference time.
Cascade reward sampling for efficient decoding-time alignment
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CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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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Gradient-Guided Reward Optimization for Inference-time Alignment
GGRO monitors token entropy to trigger gradient-guided token injection from reward models, improving LLM alignment on safety, helpfulness, and reasoning tasks at inference time.
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Multi-Objective Exploration and Preference Optimization via Mutual Information
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.