Training-free Task Classification for Multi-Task Model Merging
Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific experts into a single multi-task model. Prior work largely focuses on finding a single merged model, but it often underperforms individual experts due to parameter interference. To resolve this, dynamic model merging employs routing to activate task-relevant parameters per input. However, existing routers typically require either additional training with abundant labeled datasets or assume the access to task IDs of each input at inference time. In this work, we aim to close the gap to expert performance without additional training or task-ID...
| Authors | Jungyong Son, Jinwook Jung, Sungyong Baik |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 7 August 2026 |
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