SiM enables training-free routing in multi-task model merging by scoring test inputs via projection residuals onto SVD-based task manifolds precomputed from small support sets.
arXiv preprint arXiv:2412.00081 (2024)
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ResMerge improves merging of RL expert LLMs via a stable residual consensus backbone plus gated head correction, outperforming task-vector and spectral baselines in capability preservation.
Merging fine-tuned models for multilingual translation fails because fine-tuning redistributes language-specific neurons rather than sharpening them, increasing representational divergence in output-generating layers.
citing papers explorer
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Training-free Task Classification for Multi-Task Model Merging
SiM enables training-free routing in multi-task model merging by scoring test inputs via projection residuals onto SVD-based task manifolds precomputed from small support sets.
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ResMerge: Residual-based Spectral Merging of Large Language Models
ResMerge improves merging of RL expert LLMs via a stable residual consensus backbone plus gated head correction, outperforming task-vector and spectral baselines in capability preservation.
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One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging
Merging fine-tuned models for multilingual translation fails because fine-tuning redistributes language-specific neurons rather than sharpening them, increasing representational divergence in output-generating layers.