Not All Task Vectors Need Equal Rank: Energy-Proportional Allocation for Model Merging
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper introduces SADA-Merging, a new data‑free model merging framework that addresses limitations of existing subspace‑based methods. It allocates subspace capacity based on each task’s spectral complexity, adapts spectral preservation to task‑specific plasticity, and applies depth‑dependent anchoring to mitigate projection distortion. Experiments show that SADA‑Merging consistently outperforms current data‑free merging techniques across various task scales and adaptation settings.
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.
arXiv:2606. 19164v1 Announce Type: cross Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model.
arXiv:2607. 20561v1 Announce Type: new Abstract: LoRA adapters provide an efficient way to specialize a pretrained model for many downstream tasks, but deploying one adapter per task requires adapter storage and task selection at inference time.
arXiv:2608. 12842v1 Announce Type: new Abstract: Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining.
The paper introduces Net Utility, a data‑free metric for selecting which singular directions of low‑rank adapters (LoRAs) to keep when merging across tasks. By scoring each direction for task utility and interference, and then globally selecting the highest‑scoring directions under a total budget constraint, the method avoids the uniform‑budget assumption that hampers existing merging techniques. Experiments on vision and language tasks show that Net Utility‑based rank allocation yields about a 2% performance gain over other merging methods.