Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
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 ...
| Authors | Justin Jung |
|---|---|
| Primary category | cs.LG |
| Published | 6 October 2026 |
| Last updated at source | 8 October 2026 |
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