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.