Self-Generated Error Training for Token Editing in Diffusion Language Models
This paper addresses the training-inference mismatch in the token-to-token (T2T) editor of LLaDA2.1 diffusion language models. The proposed self-generated T2T method performs a no-gradient draft pass, fills masked positions with predicted tokens, and supervises recovery in a second pass under self-generated corruptions. Implemented via LoRA continued-pretraining on LLaDA2.1-mini, it improves accuracy while reducing edit intensity, mitigating failure modes like final-digit transcription errors and excessive self-correction.
[2606.17175] Self-Generated Error Training for Token Editing in Diffusion Language Models
[Submitted on 15 Jun 2026]
Title:Self-Generated Error Training for Token Editing in Diffusion Language Models
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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)
Submission history
From: Lin Yao [view email] [v1] Mon, 15 Jun 2026 18:13:54 UTC (435 KB)
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