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This paper introduces a novel preference-conditioned Bellman operator based on Chebyshev scalarization to compute deterministic Pareto-optimal policies for Multi-Objective Markov Decision Processes, proving its convergence and effectiveness in capturing the entire Pareto frontier.
MOCHA introduces a multi-objective optimization method for LLM agent skills, using Chebyshev scalarization and exponential annealing to handle hard platform constraints and discover Pareto-optimal variants, achieving significant improvements over existing optimizers.