elastic-weight-consolidation-bd20d3c4·2 events·first seen Aliases: Elastic Weight Consolidation
Researchers propose RECALL, an active continual learning paradigm for Vision-Language-Action (VLA) robot models that uses uncertainty-guided data collection to target states where the policy struggles, rather than passively collecting demonstrations after failures. The paper demonstrates improved fine-tuning efficiency over passive imitation learning but identifies catastrophic forgetting as a key challenge when incorporating recovery data. The authors evaluate continual learning mitigations including replay-based data mixing and elastic weight consolidation, characterizing tradeoffs between plasticity and retention in large autoregressive robot policies.
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.