On Emergent Capabilities and Model Merging

Abstract:Fine-tuned checkpoints and adapters now fill public repositories, and the most common operation applied to these artifacts is model merging: arithmetic on their weights that assembles capabilities cheaply. We ask what this operation does to emergent capabilities: behaviors an artifact carries that were never an explicit training target. Studying two independent testbeds (activation oracles and emergent-misaligned models) across three model families, we find that the answer is threefold. First, merging preserves an emergent capability that both parents carry: merging two misaligned checkpoints retains most of their broad misalignment across the whole mixing range. Second, merging cannot create an emergent capability that is superadditive in its parents: no weighted merge of two single-task oracles reaches the jointly-trained oracle's auditing ability. Third, when only one parent carries the capability, merging dilutes it faster than the trained capability that accompanies it: the gap is significant in most settings. In short, emergent behaviors of an artifact do not compose the way its trained capability does.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.24504 [cs.LG]

(or arXiv:2609.24504v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.24504 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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