Unifying Detection and Adaptation in Task-Free Continual Learning

• URL: http://arxiv.org/abs/2608.27070v1
• Date: Thu, 27 Aug 2026 12:56:27 GMT
• Title: Unifying Detection and Adaptation in Task-Free Continual Learning
• Abstract summary: We propose a textbfFisher-guided textbfFiUni framework for batch-level task detection.<n>Experiments show that FiUni can effectively infer latent batch-level task affiliations.
• Score: 8.950162084237288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, these methods often rely on explicit task boundaries during training, limiting their applicability to realistic task-free scenarios. In this paper, we propose a \textbf{Fi}sher-guided \textbf{uni}fied (\textbf{FiUni}) framework for batch-level task detection and parameter-efficient continual adaptation. FiUni is motivated by a key observation about the Fisher information matrix (FIM) of pre-trained models: the orthogonality among the principal subspaces of its Kronecker-Factored Approximate Curvature (K-FAC) approximation, estimated from a small number of downstream task samples, can reflect the similarity between different tasks. Based on this observation, FiUni constructs FIM-derived frozen subspaces to guide low-rank adaptation (LoRA), while matching the Fisher principal subspace of each incoming batch window with historical subspaces. This enables FiUni to adaptively determine whether to reuse existing knowledge, expand a related subspace, or create a new subspace, dynamically balancing knowledge sharing and task isolation. Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.

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