OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

Abstract

Generative control policies (GCPs), such as diffusion- and flow-based control policies, have proved effective parameterizations for robot learning. This work introduces Off-policy Generative Policy Optimization (**OGPO**), a sample-efficient algorithm for finetuning GCPs that maintains off-policy critics to maximize data reuse and propagate policy gradients through the full generative process of the policy via a modified PPO objective, using critics as the terminal reward. **OGPO** achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control. To our knowledge, it is also the only method that can *fine-tune poorly-initialized behavior cloning policies to near full task-success with no expert data in the online replay buffer*, and does so with *few task-specific hyperparameter tuning*. Through extensive investigations, we demonstrate that **OGPO** drastically outperforms alternative methods on policy steering and learning residual corrections, and identify the key mechanisms behind its performance. We further introduce practical stabilization tricks, including success-buffer regularization and two-sided conservative advantages to mitigate critic over-exploitation across state- and pixel-based settings. Beyond proposing **OGPO**, we conduct a systematic empirical study of GCP finetuning, identifying the stabilizing mechanisms and failure modes that govern successful off-policy full-policy improvement.