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In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conventional federated learning primarily addresses heterogeneity across clients without explicitly resolving latent task mixtures within each c...
A novel parameter-efficient approach for continual learning in LLMs is proposed, which adaptively adjusts layerwise LoRA ranks to help balance the stability–plasticity trade-off, and leverages a subspace similarity metric to evaluate the orthogonality between task-specific low-rank subspaces.
This formulation yields the first principled derivation of the log-density-ratio steering objective from a well-posed optimization problem, and consistently outperforms prior activation steering baselines while avoiding the OOD degradation commonly observed in existing methods.
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