arXiv:2605.29489v1 Announce Type: new Abstract: Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget-aware execution layer that casts LLM merging as an \emph{expert access-set} problem: given a merge operator and a checkpoint family in a shared weight coordinate system, choose which expert delta blocks to access under an explicit I/O budget. MergePipe indexes parameter blocks, builds deterministic access plans, and executes the indu
The increasing scale of LLMs and the computational cost of merging them necessitates more efficient, budget-aware methodologies for model development and deployment.
This research addresses a critical bottleneck in large language model (LLM) advancement, enabling more scalable and cost-effective development, which directly impacts the pace of AI innovation.
The focus shifts from purely algebraic operations in model merging to considering the practical I/O and budget constraints of accessing expert weights, which was previously a hidden cost.
- · LLM developers
- · Cloud providers
- · AI model infrastructure companies
- · Inefficient LLM merging techniques
- · Organizations with limited compute budgets
More efficient and faster iteration cycles for large language models will become possible.
This could democratize access to advanced LLM development by lowering the expertise and resource barriers.
The acceleration of LLM development might lead to an earlier maturation of AI agents and increasingly complex AI systems.
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Read at arXiv cs.LG