ChainLoRA: Geometry-Preserving Task Vector Merging for Continual Learning in LLMs
Continual parameter-efficient fine-tuning for large language models (LLMs) must balance retention of previously acquired knowledge, adaptation to new tasks, and strict parameter budgets. We present \textbf{ChainLoRA}, a replay-free continual merging framework built on chain-updated task-vector geometry. From a parameter-merging perspective, we formulate a geometric view of forgetting through a measurable interaction between task updates, separating directional overlap from coefficient coupling. Building on this view, ChainLoRA combines chain-updated training with post-stream adaptive SVD merging. During training, initialization and a one-sided orthogonality proxy use only the last carrier...
| Authors | Hang Yin, Haozhe Wang, Yuhua Luo et al. |
|---|---|
| Primary category | stat.ML |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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