Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
Multi-task model merging combines separately trained expert models into a single model that handles all tasks without co-training. Standard practice merges experts at their optimal validation loss. We challenge this convention by systematically studying how training duration of domain experts affects the quality of the merged model. We fine-tune experts on five domains (Math, Code, Instruction Following, Multilingual, and Safety) across three model sizes (Qwen 3.5 0.8B, 2B, and 4B), saving checkpoints from 25% to 500% of the optimal training steps and evaluating five merging methods at each duration. Our findings reveal a striking method-dependent pattern: simple averaging degrades sharpl...
| Authors | Nikita Kozodoi, Zainab Afolabi, Jack Butler |
|---|---|
| Primary category | cs.LG |
| Published | 13 July 2026 |
| Last updated at source | 15 July 2026 |
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