• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:LLMTailor: A Layer-wise Tailoring Tool for Efficient Checkpointing of Large Language Models View PDF HTML (experimental)Abstract:Checkpointing is essential for fault tolerance in training large language models (LLMs) • However, existing methods, regardless of their I/O strategies, periodically store the entire model and optimizer states, incurring substantial storage overhead and resource contention • Recent studies reveal that updates across LLM layers are highly non-uniform • Across training steps, some layers may undergo more significant changes, while others remain relatively stable or even unchanged • This suggests that selectively checkpointing only layers with significant updates could reduce overhead without harming training • Implementing such selective strategies requires fine-grained control over both weights and optimizer states, which no current tool provides

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  • Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 25 Feb 2026] Title:LLMTailor: A Layer-wise Tailoring Tool for Efficient Checkpointing of Large Language Models View PDF HTML (experimental)Abstract:Checkpointing is essential for fault tolerance in training large language models (LLMs). However, existing methods, regardless of their I/O strategies, periodically store the entire model and optimizer states, incurring substantial storage overhead and resource contention. Recent studies reveal that updates across LLM layers are highly non-uniform. Across training steps,

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