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        <title>Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning</title>
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    <abstract>The central tension in continual learning (CL) is the trade-off between plasticity (acquiring new knowledge) and stability (retaining prior knowledge). We study how a pre-trained backbone can be continually updated to absorb new knowledge while preserving existing capabilities, via capacity control: regulating the effective rank of each parameter update, a per-step quantity directly controllable inside a LoRA update. A controlled probe of LoRA rank and placement across modules and tasks reveals a consistent trade-off, with a moderate-rank sweet spot that varies by placement and task, leaving no universally optimal fixed rank; a formal bound shows forgetting grows with rank. Building on these findings, we propose Continual Dynamic Rank-Selective LoRA (CoDyRA), which jointly trains each LoRA update with rank minimization via sparsity-promoting regularization on per-component importance weights. The supervised objective drives plasticity; rank minimization regularizes forgetting. We show that rank minimization serves as an implicit forgetting regularizer in the CL regime, protecting general capability and prior-task knowledge simultaneously by controlling forgetting against the current model state. Across MTIL, X-TAIL, and TRACE (CLIP, LLaMA, Gemma), CoDyRA outperforms prior CL methods on new knowledge learning and forgetting, achieving a strong plasticity–stability balance. Project page: https://artificer-ai-lab.github.io/CoDyRA.</abstract>
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%0 Conference Proceedings
%T Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning
%A Lu, Haodong
%A Zhao, Chongyang
%A Xue, Jason
%A Yao, Lina
%A Moore, Kristen
%A Gong, Dong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lu-etal-2026-take
%X The central tension in continual learning (CL) is the trade-off between plasticity (acquiring new knowledge) and stability (retaining prior knowledge). We study how a pre-trained backbone can be continually updated to absorb new knowledge while preserving existing capabilities, via capacity control: regulating the effective rank of each parameter update, a per-step quantity directly controllable inside a LoRA update. A controlled probe of LoRA rank and placement across modules and tasks reveals a consistent trade-off, with a moderate-rank sweet spot that varies by placement and task, leaving no universally optimal fixed rank; a formal bound shows forgetting grows with rank. Building on these findings, we propose Continual Dynamic Rank-Selective LoRA (CoDyRA), which jointly trains each LoRA update with rank minimization via sparsity-promoting regularization on per-component importance weights. The supervised objective drives plasticity; rank minimization regularizes forgetting. We show that rank minimization serves as an implicit forgetting regularizer in the CL regime, protecting general capability and prior-task knowledge simultaneously by controlling forgetting against the current model state. Across MTIL, X-TAIL, and TRACE (CLIP, LLaMA, Gemma), CoDyRA outperforms prior CL methods on new knowledge learning and forgetting, achieving a strong plasticity–stability balance. Project page: https://artificer-ai-lab.github.io/CoDyRA.
%U https://aclanthology.org/2026.findings-emnlp.806/
%P 17078-17105

Markdown (Informal)

[Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning](https://aclanthology.org/2026.findings-emnlp.806/) (Lu et al., Findings 2026)

• Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning (Lu et al., Findings 2026)

ACL

• Haodong Lu, Chongyang Zhao, Jason Xue, Lina Yao, Kristen Moore, and Dong Gong. 2026. Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17078–17105, Budapest, Hungary. Association for Computational Linguistics.