Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL

TL;DR

ROVED is a hybrid framework that combines vision-language embeddings with targeted oracle feedback to reduce the cost of preference-based reinforcement learning, achieving high efficiency and generalization across robotic tasks.

Contribution

The paper introduces ROVED, a novel hybrid approach that leverages VLE models with selective oracle feedback and efficient fine-tuning to improve preference-based RL scalability and accuracy.

Findings

ROVED reduces oracle queries by up to 80% in robotic tasks.

The adapted VLE generalizes across tasks, saving up to 90% in annotations.

ROVED matches or surpasses prior methods in effectiveness.

Abstract

Preference-based reinforcement learning can learn effective reward functions from comparisons, but its scalability is constrained by the high cost of oracle feedback. Lightweight vision-language embedding (VLE) models provide a cheaper alternative, but their noisy outputs limit their effectiveness as standalone reward generators. To address this challenge, we propose ROVED, a hybrid framework that combines VLE-based supervision with targeted oracle feedback. Our method uses the VLE to generate segment-level preferences and defers to an oracle only for samples with high uncertainty, identified through a filtering mechanism. In addition, we introduce a parameter-efficient fine-tuning method that adapts the VLE with the obtained oracle feedback in order to improve the model over time in a synergistic fashion. This ensures the retention of the scalability of embeddings and the accuracy of oracles, while avoiding their inefficiencies. Across multiple robotic manipulation tasks, ROVED matches or surpasses prior preference-based methods while reducing oracle queries by up to 80%. Remarkably, the adapted VLE generalizes across tasks, yielding cumulative annotation savings of up to 90%, highlighting the practicality of combining scalable embeddings with precise oracle supervision for preference-based RL.