Escaping Low-Dimensional Overlap: Multi-Task Model Merging via High-Dimensional Sparse Disentanglement

• URL: http://arxiv.org/abs/2608.25354v1
• Date: Wed, 26 Aug 2026 04:16:31 GMT
• Title: Escaping Low-Dimensional Overlap: Multi-Task Model Merging via High-Dimensional Sparse Disentanglement
• Abstract summary: Model merging provides an efficient way to construct multi-task generalist models without additional training, but its performance often degrades under severe task interference.<n>In this paper, we propose a sparse-representation-based merging framework that usesSAEs to project task vectors into a high-dimensional feature space.<n>Experiments on both Qwen2.5-1.5B and Qwen5-7B demonstrate that our method consistently outperforms representative baselines.
• Score: 5.048419950197785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model merging provides an efficient way to construct multi-task generalist models without additional training, but its performance often degrades under severe task interference. Task interference in model merging primarily stems from \textit{superposition}, where task-specific features become entangled within the parameter space. This entanglement renders conventional decomposition methods insufficient for effectively isolating useful task directions from interfering components. In this paper, we propose a sparse-representation-based merging framework that uses Sparse Autoencoders (SAEs) to project task vectors into a high-dimensional sparse feature space, enabling feature-level disentanglement before fusion. To reduce computational overhead, we further introduce a lightweight Group-Ranked Zeroth-Order Optimizer (GR-ZOO) to identify task-critical layers for selective merging. Experiments on both Qwen2.5-1.5B and Qwen2.5-7B demonstrate that our method consistently outperforms representative baselines, including Task Arithmetic, TIES-Merge, DARE, Fisher-Merge,and several recent training-free merging methods, across mathematical reasoning, code generation, instruction following, and general knowledge tasks. In a highly conflicting four-task setting on Qwen2.5-1.5B, our method further achieves a 2.78\% improvement over the strongest baseline.

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