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Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models — arXiv:2608.08082

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08082
License (as archived): CC0-1.0
Archived: 2026-10-19T08:49:52.488Z
SHA-256 of the archived text: 563a68f98f1c6802221119b17654643b58b21cecfcbd7e9457422c5655c50f00
Stable URL of this page: https://metraipolis.com/documento/dc_7138317ee89c

How to cite

Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models — arXiv:2608.08082 — arXiv (en), https://arxiv.org/abs/2608.08082 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7138317ee89c (Metraipolis archive dc_7138317ee89c, sha256 563a68f98f1c6802221119b17654643b58b21cecfcbd7e9457422c5655c50f00)

BibTeX

@misc{dc7138317ee89c,
  title = {Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models — arXiv:2608.08082},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08082},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7138317ee89c; sha256 563a68f98f1c6802221119b17654643b58b21cecfcbd7e9457422c5655c50f00},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08082. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Classifier-free guidance (CFG) is usually kept on throughout masked diffusion language model decoding, although its benefit varies across prompts and over time. We study when CFG is actually needed by comparing, from any partial output, the probability of eventual constraint satisfaction under continued CFG and under base-only continuation. Their difference defines the remaining value of guidance. Guidance dependence is highly prompt-specific. Many prompts already succeed without CFG, while for others it provides no measurable benefit or can be harmful. For prompts that do benefit, the gain is often concentrated early. We define the commitment horizon $\astar$ as the earliest point from which switching all remaining decoding to the base model reduces final success by no more than a chosen tolerance. Under the base model, the corresponding success probability, or committor, is a martingale. To first order, CFG's per-step effect is governed by the covariance between the guidance logit direction and the successor committor. This gives a local account of when guidance can help, but it does not by itself locate the horizon. Among prompts with an observed preterminal horizon, $\astar$ is usually early and varies more within constraint families than between them. Freezing each prompt at its own cross-fitted horizon is noninferior to full CFG on all 13 subtasks at the prespecified margin, even while many tokens remain masked. This separates commitment from realization. The boundary also identifies a later region in which higher parallelism adds only a small cost in constraint success, although fluency still degrades with parallel width. For failed trajectories, reopening committed positions improves recovery in both failure modes.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2608.08082 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:49:41.844Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).