On Emergent Capabilities and Model Merging

The paper investigates how model merging—an arithmetic operation on the weights of fine‑tuned checkpoints and adapters—affects emergent capabilities, which are behaviors not explicitly trained for. Across two testbeds and three model families, the authors find that merging preserves shared emergent capabilities, cannot create superadditive capabilities, and tends to dilute a capability that is present in only one parent. Thus, emergent behaviors do not compose in the same way as trained capabilities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• fine-tuning