PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
Debabrata Mahapatra and Vaibhav Rajan
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization
PCD is a new gradient-based optimizer for hierarchical multi-objective problems that prioritizes primary descent with minimal controlled distortion for secondary objectives via a single tau parameter.
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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.