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arXiv · 2509.05056

Masked Diffusion Language Models with Frequency-Informed Training

Despoina Kosmopoulou·Efthymios Georgiou·Vaggelis Dorovatas·Georgios Paraskevopoulos·Alexandros Potamianos
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Abstract

We present a masked diffusion language modeling framework for data-efficient training for the BabyLM 2025 Challenge. Our approach applies diffusion training objectives to language modeling under strict data constraints, incorporating frequency-informed masking that prioritizes learning from rare tokens while maintaining theoretical validity. We explore multiple noise scheduling strategies, including two-mode approaches, and investigate different noise weighting schemes within the NELBO objective. We evaluate our method on the BabyLM benchmark suite, measuring linguistic competence, world knowledge, and human-likeness. Results show performance competitive to hybrid autoregressive-masked baselines, demonstrating that diffusion-based training offers a viable alternative for data-restricted language learning.

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Despoina Kosmopoulou, Efthymios Georgiou, Vaggelis Dorovatas, Georgios Paraskevopoulos, Alexandros Potamianos. 2025-09-05. Masked Diffusion Language Models with Frequency-Informed Training. https://arxiv.org/abs/2509.05056

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