Task-specific LoRA adapters in continual learning exhibit significant low-rank subspace overlap, enabling LiteLoRA's learned gating to reduce active adapters by 20-70% while matching or exceeding prior performance.
Lori: Reducing cross- task interference in multi-task low-rank adaptation,
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HELLoRA selectively applies LoRA adapters to hot experts in MoE layers, using as little as 15.7% of standard LoRA parameters while improving accuracy by 9.2% on OlMoE across math, code, and alignment tasks.
AlignFed introduces a multi-stage semantic alignment mechanism for asynchronous federated fine-tuning of LLMs to mitigate model drift, client drift, and aggregation unfairness in heterogeneous edge environments.
citing papers explorer
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When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning
Task-specific LoRA adapters in continual learning exhibit significant low-rank subspace overlap, enabling LiteLoRA's learned gating to reduce active adapters by 20-70% while matching or exceeding prior performance.
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HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
HELLoRA selectively applies LoRA adapters to hot experts in MoE layers, using as little as 15.7% of standard LoRA parameters while improving accuracy by 9.2% on OlMoE across math, code, and alignment tasks.