Model Merging with Functional Dual Anchors

Model Merging Made Simple with Functional Dual Anchors

Ever wished your favorite AI could learn from different versions without breaking? New way uses model merging but it doesn't mash together the inner parts, it nudges behavior with tiny example inputs.
These are called Functional Dual Anchors, short clips of input that teach a model how to act like a tuned version, without changing the whole brain.
The anchors are actually synthetic inputs that push the model toward the right moves, so separate tools can share task knowledge while staying stable.
This makes combining models easier and gives more robustness, yet stays flexible if you want to tune more later.
It feels like adding short notes to a recipe instead of rewriting the cookbook, and it works well even when different versions disagree.
You can mix and match tuned models faster, and keep the base model mostly intact, so the result is practical and less risky.
Small trick, big effect — a simple way to blend skills without breaking what already works.

Read article comprehensive review in Paperium.net:
Model Merging with Functional Dual Anchors

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