Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers

The real advance here is not merging itself but the bidirectional framing: prior LMC work assumed one model was the fixed reference point, which introduced asymmetric loss barriers that grew catastrophically with parameter count. Making both models co-optimize toward the interpolation path is what actually breaks the scaling wall.

This connects directly to the optimizer work covered in 'Muown Implicitly Performs Angular Step-size Decay' from the same day. Both papers are attacking the same underlying problem from different angles: how to maintain stable, well-behaved parameter trajectories in very large transformers. Muown's angular step-size decomposition is about controlling directional updates during training, while LMC alignment is about reconciling directional divergence after training. Together they suggest the field is converging on a more geometric view of weight space, where the shape of the optimization path matters as much as the endpoint. The self-filtering data curation work covered in 'Data Selection Through Iterative Self-Filtering' is also adjacent, since cleaner training distributions should in principle produce models whose weight geometries are easier to align post-hoc.

Watch whether any of the major open-weight model families, Llama or Mistral variants specifically, publish merge experiments using this bidirectional alignment method within the next two quarters. Adoption there would confirm the method generalizes beyond controlled research conditions.