MeRoTune: RoPE-Safe Merging with a Tunable Dial
When you merge two fine-tuned models from the same base checkpoint by simply averaging their weights, you implicitly assume their attention subspaces are still aligned. Recent work attempts to fix misalignments by learning an invertible correction matrix, $M$, for each model's query and key projections. This correction cancels out---using $M$ on the query side and $M^{-T}$ on the key side---right before the dot product. However, this cancellation is only exact if nothing sits between the projection and the dot product. In reality, almost all modern open-weight language models put a rotary position embedding (RoPE) exactly there. In this paper, we show that this cancellation is exact under...
| Authors | Salman Faroz |
|---|---|
| Primary category | cs.CL |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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