openvla-oft-43e5a91f·2 events·first seen Aliases: OpenVLA-OFT
Patch Policy is a new robot learning architecture that enables transformer-based policies to consume dense pre-trained Vision Transformer patch tokens directly, without the overhead of a full vision-language model backbone. A block-causal attention mask preserves temporal causality while allowing the model to attend over many patch tokens per observation. Across four simulated and three real-world environment suites, the method achieves a 40% relative improvement over global-pooled representation baselines and outperforms fine-tuned OpenVLA-OFT by 18% while using roughly 0.7% of its parameters. The work addresses a practical gap between lightweight robot policies and expensive VLA models.
Researchers from UT Austin, UCLA, Nanyang Technological University, and Sony developed a sequential fine-tuning recipe combining LoRA and on-policy reinforcement learning (GRPO) to reduce catastrophic forgetting in vision-language-action (VLA) models for robotics. Applied to the OpenVLA-OFT model on the LIBERO benchmark, the method achieved 81.2% success on libero-spatial tasks with near-zero forgetting (0.3 percentage point drop), outperforming established continual learning baselines including Dark Experience Replay and Elastic Weight Consolidation. The approach requires no replay of prior task data and also showed modest generalization to unseen tasks. The authors note the method has not yet been tested outside robotics simulation contexts.