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arXiv · 2609.22253

CAMFT: Conflict-Aware Mergeable Fine-Tuning for Large Language Models

Jingang Zhou·Haiyang Guo·Yuan Ma·Han Zhu·Xu-Yao Zhang
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Abstract

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.

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Jingang Zhou, Haiyang Guo, Yuan Ma, Han Zhu, Xu-Yao Zhang. 2026-09-06. CAMFT: Conflict-Aware Mergeable Fine-Tuning for Large Language Models. https://arxiv.org/abs/2609.22253

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