Loopholing All the Way: Language Modeling with pure Self-Conditioning
Recent Diffusion Language Models (DLMs) use Self-Conditioning (SC), or Loopholing, to improve their performance. While SC is traditionally seen as an auxiliary heuristic, its empirical success raises the question: can a continuous generative model be based purely on Self-Conditioning? We answer affirmatively with LAW (``Loopholing All the Way''), which propagates the Expected Embedding under the denoiser's prediction across steps, decoding the tokens once, at the end. On Sudoku, LAW outperforms previous Discrete and Continuous Diffusion Models, including Loopholing Discrete Diffusion. On Maze, LAW is the most accurate from 2 to 64 NFE. After training on TinyGSM and sampling at temperature...
| Authors | Adrien Deschenaux, Nadezhda Ilieva, Justin Deschenaux et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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