Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models

Abstract: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.

Computation and Language (cs.CL)

arXiv:2609.25890 [cs.CL]

(or arXiv:2609.25890v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.25890 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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