Short-to-long training is a simple curriculum for speech models, but its gains can be difficult to interpret.
In speech token language models, length-based training can change the shuffle policy, batch composition, token retention, and token weights under batch-mean loss.
We disentangle these factors through matched comparisons.
In the tested settings, short-to-long ordering shows no independent benefit when batch composition and token exposure are fixed.
First-epoch grouping lowers perplexity for Mimi under batch-mean loss, but this gain is not observed under token-balanced loss.
The cross-tokenizer results are consistent with a link between chunk-length variation and token weighting.
This work provides a systematic analysis protocol for studying length-based training in variable-length speech models.