Pursuing the Pareto Frontier for Interactive Intervention with LLM-based Reward Design

Reward design is challenging when agents must balance multiple deployment objectives. In interactive intervention, a useful reward should sustain task performance across a broad range of expert-assistance budgets, yet existing LLM-based methods rank rewards with a scalar objective, reducing multi-objective reward design to single-point evaluation. We introduce PARSE, a Pareto-aware framework that evaluates each reward by the preference-response set induced by a single preference-conditioned policy. For every candidate reward, one policy is trained across preferences, screened for Interaction Rate span and monotonicity, and evaluated by the hypervolume contribution of its nondominated operating points. Trajectory diagnoses of failed and inefficient episodes then guide reward mutation and crossover, while global exploration maintains search diversity. On PointNav in unseen Habitat scenes, PARSE attains the highest hypervolume among methods with multi-point trade-off sets and reaches $82.8\%$ Success at a $20\%$ Interaction Rate, outperforming the strongest baseline by $4.9$ percentage points. The selected reward also transfers unchanged to ObjectNav, where it outperforms no-help and random-help baselines.