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
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.