CoMerge: Conflict-Driven Preference Optimization for Multi-Task Model Merging
Quick summary
arXiv:2609.02273v1 Announce Type: new Abstract: Model merging provides an efficient paradigm for constructing multi-task large language models (LLMs) without full model retraining, yet it remains challenged by parameter interference. While existing methods aim to preserve the capabilities of individual expert models and mitigate interference, they generally do not directly learn from the potentially degraded behaviors exposed by naive merging. In this paper, we propose a conflict-driven preference optimization framework for model merging (CoMerge), which reformulates model merging as a prefere
Key takeaways
- arXiv:2609.02273v1 Announce Type: new Abstract: Model merging provides an efficient paradigm for constructing multi-task large language models (LLMs) without full model retraining, yet it remains challenged by parameter interference.
- While existing methods aim to preserve the capabilities of individual expert models and mitigate interference, they generally do not directly learn from the potentially degraded behaviors exposed by naive merging.
- In this paper, we propose a conflict-driven preference optimization framework for model merging (CoMerge), which reformulates model merging as a prefere
Why it matters
“CoMerge: Conflict-Driven Preference Optimization for Multi-Task Model Merging” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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