ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories

Abstract:Studying model training requires checkpoint trajectories, whose storage cost limits temporal resolution. Although adjacent checkpoints are numerically close, existing lossless codecs either operate on XOR bit patterns or ignore trajectory-specific conditional structure. We show that a reference weight's exponent predicts the bit length of its displacement in ordered floating-point space without side information. We introduce \textbf{ULPack}, a lossless codec which exploits this dependence by conditioning entropy models of ordered-integer residuals on the reference exponent. Across bfloat16, float16, and float32 trajectories, ULPack reduces storage by up to 27\% against ZipNN, 21\% against zstd-19, and 11.5\% against FM-Delta. Across two dense trajectories, compression improves as updates shrink and reference conditioning strengthens, while near uniform low-order bits set the remaining compression floor and earlier checkpoints add little information.

Machine Learning (cs.LG)

arXiv:2610.14184 [cs.LG]

(or arXiv:2610.14184v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14184 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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