VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
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arXiv:2606.17999v1 Announce Type: new Abstract: MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregressive convention of using repeated \texttt{[EOS]} tokens for padding during instruction tuning, giving \texttt{[EOS]} a dual role as both a semantic terminator and a padding token. We show that this dual role is a root cause of \texttt{[EOS]} overflow under large-block decoding. To decouple these roles, we propose VoidPadding, which introduces \texttt{[VOID]} for padding a
The continuous evolution of large language models and their architectural design necessitates ongoing research into improving efficiency and reducing inherent limitations.
This research addresses a fundamental issue in the architecture of masked diffusion language models, potentially leading to more stable and efficient model training and inference.
The proposed 'VoidPadding' method disentangles the semantic termination role from the padding role of the '[EOS]' token, which could improve model performance and prevent specific errors like '[EOS]' overflow.
- · AI model developers
- · Researchers in NLP
- · Users of MDLMs
- · Existing MDLMs with '[EOS]' overflow issues
Improved stability and predictability in masked diffusion language model behavior.
Faster iteration and deployment of new MDLMs due to cleaner architectural design.
Enhanced capability for specific applications relying on very long or complex generative text outputs.
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Read at arXiv cs.CL