CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning

Abstract

Large language models (LLMs) can acquire new capabilities through fine-tuning, but continual adaptation often leads to catastrophic forgetting. We propose CRAFT, a continual learning framework that avoids updating model weights by instead learning low-rank interventions on hidden representations. CRAFT proceeds in three stages: it first routes each task to a group of similar tasks based on output-distribution divergence; it then fine-tunes the model using a Kullback-Leibler (KL) divergence against the group's prior state, which directly controls forgetting and determines convergence; finally, it merges interventions for the updated task into the shared representation using the same KL signal. This design unifies routing, regularization, and merging through a single KL-based objective. CRAFT improves overall performance and reduces forgetting compared to strong LoRA-based approaches across multiple benchmarks and model scales, while remaining robust to task ordering. These results suggest that controlling adaptation in representation space, guided by output-space divergence, provides a scalable and principled approach to continual learning in LLMs.

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BibTeX

@article{hossen2026craft,
  title = {CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning},
  author = {Md Anwar Hossen and Fatema Siddika and Juan Pablo Munoz and Tanya Roosta and Ali Jannesari},
  year = {2026},
  abstract = {Large language models (LLMs) can acquire new capabilities through fine-tuning, but continual adaptation often leads to catastrophic forgetting. We propose CRAFT, a continual learning framework that avoids updating model weights by instead learning low-rank interventions on hidden representations. CRAFT proceeds in three stages: it first routes each task to a group of similar tasks based on output-distribution divergence; it then fine-tunes the model using a Kullback-Leibler (KL) divergence again},
  url = {https://arxiv.org/abs/2605.05732},
  keywords = {cs.LG, cs.AI},
  eprint = {2605.05732},
  archiveprefix = {arXiv},
}

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