catastrophic forgetting
catastrophic-forgetting-f9893c37·2 events·first seen 1mo agoAliases: catastrophic forgetting
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Self-Generated Replay Nearly Eliminates Catastrophic Forgetting in Language Models
This paper investigates catastrophic forgetting in language models during continual learning, finding that models can use self-generated samples from their own training distribution as effective replay data, nearly eliminating forgetting without requiring stored exemplars. The authors identify two key conditions where forgetting persists: when models are pretrained near capacity saturation (leaving no room for new knowledge), and when low learning rates are used to reduce forgetting at the cost of requiring far more training steps. Self-generated replay breaks this learning-rate/forgetting tradeoff, enabling fast high-learning-rate finetuning without degradation on prior tasks.
Sony and University Researchers Train Robots To Learn Without Catastrophic Forgetting
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.