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Source note3 min read

Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers

Source

arXiv

arxiv.org/abs/2606.23607v1

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Source

Plain-Language Brief

The authors propose a method for scaling Linear Mode Connectivity (LMC) to allow the merging of billion-parameter pretrained transformers, improving interpolation performance. The approach involves optimizing weight transformations and bidirectional learning to reduce interpolation barriers across large-scale architectures.

What the Document Says

  • The paper discusses Linear Mode Connectivity (LMC) as a method for understanding and merging neural networks.
  • Existing LMC methods optimize the interpolation path from only one model endpoint, which limits scalability and effectiveness for large pretrained transformers.
  • The authors propose a new framework to enable LMC-based model merging for billion-parameter pretrained transformers.
  • They apply weight transformations to align functionally equivalent solutions and introduce a dual learning procedure for bidirectional optimization.
  • This bidirectional optimization reduces interpolation barriers, enabling reliable merging across large-scale architectures.
  • The method is empirically shown to achieve near-zero loss barriers on WikiText for medium-sized language models, representing the first demonstration of near-barrier-free linear connectivity at this scale.
  • In the vision domain, ViT-L maintains above 69% ImageNet top-1 accuracy throughout the interpolation path while modern billion-parameter LLMs exhibit only small loss barriers.
  • The results suggest that properly resolving parameter symmetries enables large pretrained Transformers to be connected and merged through simple linear paths with improved interpolation performance.

Key Concepts Mentioned

  • Linear Mode Connectivity
  • pretrained transformers
  • weight transformations
  • dual learning
  • interpolation path

Claims Made by the Document

  • The proposed method enables LMC-based model merging for billion-parameter pretrained transformers.
  • Bidirectional optimization reduces interpolation barriers and enables reliable merging across large-scale architectures.
  • Near-zero loss barriers are achieved on WikiText for medium-sized language models.
  • ViT-L maintains high ImageNet top-1 accuracy throughout the interpolation path while modern billion-parameter LLMs exhibit small loss barriers.

Evidence Used

  • The authors explain their method and its application in the paper.

Methods / Approach

  • Properly parameterized functionality-preserving weight transformations
  • Bidirectional optimization through dual learning

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The method's scalability may be limited by the computational resources required for training and optimizing billion-parameter models.

Open Questions

  • How can the proposed method be further optimized to improve its scalability?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper does not provide explicit details on the computational complexity of their method.
  • Further investigation is needed to understand the limitations and potential improvements of the proposed approach.

Raw Abstract / Excerpt

Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the interpolation path from only one model endpoint, limiting their scalability and effectiveness for large pretrained transformers. We propose a novel and scalable framework for enabling LMC-based model merging to {\em billion-parameter pretrained transformers}. Our method applies properly parameterized functionality-preserving weight transformations to align functionally equivalent solutions, and introduces a dual learning procedure in which both models jointly learn their corresponding transformations toward a shared linear interpolation path. This bidirectional optimization substantially reduces interpolation barriers and enables more reliable merging across large-scale architectures. Empirically, we show that our approach achieves near-zero loss barriers on WikiText for language models with medium-sized parameters, representing, to our knowledge, the first demonstration of near-barrier-free linear connectivity at this scale. In the vision domain, ViT-L maintains above 69% ImageNet top-1 accuracy throughout the interpolation path, while modern billion-parameter LLMs exhibit only small loss barriers. These results suggest that properly resolving parameter symmetries enables large pretrained Transformers to be connected and merged through simple linear paths with substantially improved interpolation performance. Code: https://github.com/VILA-Lab/Dual-Learned-Matching .