arXiv:2605.26484v1 Announce Type: new Abstract: Model merging has emerged as a lightweight paradigm for enhancing Large Language Models (LLMs), yet its underlying mechanisms remain poorly understood. In this work, we analyze late-stage pre-training trajectories and uncover a \textbf{Rank-1 Subspace} phenomenon: while raw optimization steps oscillate violently, consecutive \emph{merged} checkpoints collapse onto a stable, approximately one-dimensional linear manifold. We theoretically ground this observation in a \emph{river-valley} landscape analysis: averaging acts as a geometric low-pass fil
This research emerges as the field of Large Language Models (LLMs) matures and optimization techniques like model merging become crucial for efficiency and performance.
Understanding the fundamental mechanisms of model merging can significantly improve the development, deployment, and scalability of LLMs, impacting efficiency and interpretability.
The theoretical grounding of a 'Rank-1 Subspace' and 'river-valley' landscape provides a deeper theoretical understanding of LLM training and merging dynamics, allowing for more robust and efficient model development.
- · AI researchers
- · LLM developers
- · Cloud providers
- · AI-driven product companies
- · Inefficient LLM training methodologies
- · Organizations with limited compute resources
Improved model merging techniques will lead to more optimized and powerful LLMs.
This could accelerate the deployment of complex AI agents and reduce their operational costs.
More efficient LLMs might enable new applications in resource-constrained environments or democratize access to advanced AI capabilities.
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Read at arXiv cs.LG