Model merging has emerged as a promising paradigm for integrating multiple task-specific capabilities into a single large language model.
However, existing methods predominantly focus on post-hoc processing of independently fine-tuned models, overlooking how the training phase itself impacts cross-task compatibility.
Resolving parameter conflicts after fine-tuning is inherently sub-optimal.
To address this, we propose CAMFT, a Conflict-Aware Mergeable Fine-Tuning method that makes task adaptation both efficient and mergeaware.
CAMFT treats mergeability as a property shaped during fine-tuning, rather than only a problem to be solved after fine-tuning.
By guiding each task to update sparse coordinates with lower cross-task conflict, CAMFT produces task updates that are efficient to train and more compatible for downstream model merging.
Extensive experiments demonstrate that CAMFT outperforms standard finetuning baselines in multi-task merging scenarios.
Codes are available at https://github.com/gyanchow/CAMFT-LLM.