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New language models learn tokenization for improved pretraining efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two new subword segmental language models, SubSegGPT and SubSegDeBERTa, for the 2026 BabyLM Challenge. These models learn tokenization during the pretraining phase, allowing them to discover optimal subword units. SubSegDeBERTa showed notable gains in zero-shot evaluation for the Strict track, while SubSegGPT outperformed tokenization-based baselines on the Strict-small track. The findings suggest that learnable subword tokenization enhances sample-efficiency in language model pretraining. AI

IMPACT Demonstrates potential for improved sample-efficiency in language model pretraining through learned tokenization.

RANK_REASON Academic paper detailing new models and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New language models learn tokenization for improved pretraining efficiency

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Academic paper detailing new models and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Francois Meyer ·

    Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining

    arXiv:2609.01151v1 Announce Type: new Abstract: In the standard LM training pipeline, subword tokenisation is applied as a preprocessing step. Subword segmental language modelling is an alternative paradigm in which tokenisation is learned during training, allowing the model to d…

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