The paper investigates how synthetic data used for fine‑tuning large language model (LLM) skill routers can lead to catastrophic forgetting of real and out‑of‑distribution (OOD) skills. By evaluating a production router with 34,396 skills, the authors show that synthetic‑data fine‑tuning improves in‑distribution retrieval but degrades performance on real and OOD data. They test several continual‑learning inspired mitigation methods—embedding‑anchor regularization, Learning without Forgetting, Elastic Weight Consolidation, and L2‑initialization—and find that these approaches both preserve OOD retrieval performance and boost synthetic in‑distribution retrieval by up to 13.98% for a 0.6B Qwen retriever and reranker.
arXiv:2610.17726v1 Announce Type: cross Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new...
arXiv:2605. 05285v2 Announce Type: replace Abstract: Large language models (LLMs) often suffer from catastrophic forgetting in continual learning: after learning new tasks sequentially, they perform worse on earlier tasks.
NADS (Navigator‑Guided Data Selection) is a fine‑tuning framework for large language models that mitigates catastrophic forgetting when the original pretraining data are unavailable. It first trains a navigator model without preservation regularization to detect capability drift, then scores synthetic data candidates using the predictive divergence between the navigator and the pretrained model. Finally, it selects a diverse, utility‑aware subset of synthetic data and fine‑tunes while distilling pretrained behavior, achieving a better balance between new‑task performance and general capability preservation with reduced selection overhead.
The paper introduces EoupCT, a framework that estimates and orthogonalizes unknown pre‑training gradients to mitigate catastrophic forgetting during continual fine‑tuning of large language models. It generates pseudo data most susceptible to forgetting using a learnable soft prompt with Gumbel‑Softmax, then jointly optimizes model parameters and the prompt via a first‑order Pareto optimizer to enforce orthogonality between new task updates and the estimated gradients. Experiments on multiple LLMs show that EoupCT preserves both task‑specific performance and the models’ inherent general‑purpose knowledge.
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
arXiv:2610.02126v1 Announce Type: cross Abstract: We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of...
arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.
CRAFT is a continual learning framework that freezes model weights and learns low‑rank interventions on hidden representations to mitigate catastrophic forgetting. Tasks that are similar share an intervention, determined by how they change the model’s output distribution, and a KL penalty keeps new tasks close to existing behavior. The approach also allows task identification at test time without explicit labels and shows improved performance and reduced forgetting across benchmarks compared to LoRA‑based methods.
arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...
The paper introduces Skill Schema Transport (SST), a post‑training framework that mitigates catastrophic forgetting in continual domain adaptation by translating new domain knowledge into a model’s existing skill space. SST abstracts each example type into a reusable skill schema, preserving essential operations while removing domain‑specific surface forms, thereby turning knowledge updates into procedural reuse rather than trajectory memorization. Experiments on two 3B instruction‑tuned backbones show that SST consistently reduces forgetting and improves generalization, achieving up to a 5.75‑point performance gain and a dramatic drop in maximum forgetting from 42.41 to 7.10 points on the Qwen‑3B MATH stream.
arXiv:2602.03493v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) methods have emerged as crucial techniques for adapting large pre-trained models to downstream tasks under computational...