Self-Generated Error Training for Token Editing in Diffusion Language Models
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Computer Science > Computation and Language
Title:Self-Generated Error Training for Token Editing in Diffusion Language Models
Abstract:Token-to-token (T2T) editing lets LLaDA2.1 revise committed tokens during block-diffusion decoding. The released recipe trains this editor on random vocabulary corruptions, but at inference the editor sees the model's own fluent, high-confidence draft errors instead. We study this training-inference mismatch and propose self-generated T2T, which performs a no-gradient draft pass, fills masked positions with predicted tokens, and supervises recovery in a second pass under these self-generated corruptions. We implement the update as a short LoRA continued-pretraining pass on LLaDA2.1-mini and evaluate on several benchmarks under the official Q-Mode T2T procedure with unchanged inference parameters. The method generally improves accuracy while reducing T2T edit intensity, mitigating failure modes such as final-digit transcription errors after otherwise correct reasoning and excessive self-correction before short factual answers.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.17175 [cs.CL] |
| (or arXiv:2606.17175v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17175
arXiv-issued DOI via DataCite (pending registration)
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