Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
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Computer Science > Machine Learning
arXiv:2610.09145 (cs)
[Submitted on 6 Oct 2026]
Title:Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
Authors:Justin Jung
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Abstract:We revisit a standard accepted practice in the continuous diffusion language model
literature of fixing conditioning prompt tokens clean during training.
We make a very simple modification: also noise the conditioning prompt tokens during training.
We demonstrate that under this modified training objective, we achieve better generalization
in combinatorial reasoning tasks such as Sudoku and N-Queens, with the largest gains on harder variants
($3.73\% \to 24.65\%$ solve rate on Sudoku Hard), and increased diversity of generated solutions ($50.60\% \to 73.79\%$ coverage on
10x10 N-Queens). We also show measurable improvements to natural language generation quality
in modest dataset regimes with Gigaword summarization, but notably demonstrate that gains do not
transfer to all natural language tasks (e.g open ended dialogue generation).
Our method is a single line change to the training objective, requires no additional inference costs by default,
and provides the flexibility of classifier-free guidance inspired guided sampling. Our
\href{this https URL} {code} is publicly available.
| Comments: | Published in Transactions on Machine Learning Research (TMLR), 2026 |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.09145 [cs.LG] |
| (or arXiv:2610.09145v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09145
arXiv-issued DOI via DataCite (pending registration)
|
|
| Journal reference: | Transactions on Machine Learning Research (2026) |
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View a PDF of the paper titled Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models, by Justin Jung
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