Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL
This addresses scalability issues in preference-based RL for robotics, offering a practical solution with significant annotation savings, though it is incremental in combining existing techniques.
The paper tackles the high cost of oracle feedback in preference-based reinforcement learning by proposing ROVED, a hybrid framework that combines vision-language embeddings with targeted oracle queries, reducing oracle queries by up to 80% while matching or surpassing prior methods on robotic manipulation tasks.
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.