Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models
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Computer Science > Computation and Language
arXiv:2609.25890 (cs)
[Submitted on 22 Sep 2026]
Title:Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models
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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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.25890 [cs.CL] |
| (or arXiv:2609.25890v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.25890
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models, by Hongjin Song and 6 other authors
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