CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Robust multi-objective controlled decoding of large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Multi-Objective Control trains a single LLM as a preference-conditioned policy using multi-objective optimization in RLHF to produce outputs in user-specified regions of the Pareto front.
citing papers explorer
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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.
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One Model for All: Multi-Objective Controllable Language Models
Multi-Objective Control trains a single LLM as a preference-conditioned policy using multi-objective optimization in RLHF to produce outputs in user-specified regions of the Pareto front.