Paper recorded by Signals 4 on 2026-10-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16
Category: cs.LG · 机器学习 · first seen 2026-10-16
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to continual learning in LLMs. Recent works connect forgetting to how far, and along which directions, fine-tuning moves the weights. On-policy methods such as reinforcement learning and on-policy distillation avoid updating the