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Will model merging create one model to rule them all?
Model Merging – An Open Recipe
Published: 12 May 2026 · Updated: 13 July 2026
Read the original sourceWhat the paper says
This paper proposes a method for merging the weights of two LLMs trained on different tasks. By aligning and averaging layers, they claim to create a single model that retains capabilities from both.
The Critique
Weight merging can lead to interference and catastrophic forgetting. The authors provide limited analysis of failure cases and do not explore how merging affects safety or bias.
Why It Matters
If successful, model merging could reduce the need to train new models from scratch and accelerate knowledge sharing across domains.
What They Missed
There is no discussion of intellectual property or licensing issues when merging models trained on proprietary data. The authors also omit an evaluation of interpretability after merging.
The Big Question
Can we safely merge models without inadvertently combining their worst behaviors or violating data policies?