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The Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model — arXiv:2610.40847

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40847
License (as archived): CC0-1.0
Archived: 2026-11-03T05:32:20.962Z
SHA-256 of the archived text: 2172e9ce580e01c3b0c989d4c3b01012af4f0a56a3cb5cf12cd61b3c0e317d6e
Stable URL of this page: https://metraipolis.com/documento/dc_eb6cebb79101

How to cite

The Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model — arXiv:2610.40847 — arXiv (en), https://arxiv.org/abs/2610.40847 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb6cebb79101 (Metraipolis archive dc_eb6cebb79101, sha256 2172e9ce580e01c3b0c989d4c3b01012af4f0a56a3cb5cf12cd61b3c0e317d6e)

BibTeX

@misc{dceb6cebb79101,
  title = {The Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model — arXiv:2610.40847},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40847},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb6cebb79101; sha256 2172e9ce580e01c3b0c989d4c3b01012af4f0a56a3cb5cf12cd61b3c0e317d6e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40847. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Continuous diffusion language models (CDLMs) and speculative drafters offer efficient language generation but are typically evaluated differently: CDLMs generate independently through iterative refinement, whereas drafters propose text for target-model verification and correction. We compare their architectures without verification, using LangFlow for CDLMs and adapting DSpark's Markov and recurrent-head architectures for standalone generation. All models are trained from scratch for unconditional, single-block OpenWebText generation at lengths 16, 128, and 1024, without target features or verification, using common preprocessing, backbone dimensions, and training-update budgets. We evaluate generation perplexity (GenPPL) with an external language model and distributional similarity to reference text with MAUVE. With default denoising steps equal to sequence length, LangFlow outperforms both DSpark variants on both metrics at lengths 128 and 1024, while DSpark-RNN leads at 16. A conditional KL analysis characterizes dependencies excluded by the Markov head's previous-token conditioning. Recurrent memory removes this restriction and improves both metrics, but the longer-sequence gap persists. Teacher probability matching further lowers Markov GenPPL without observed MAUVE gains. Increasing LangFlow's denoising steps at fixed weights improves both metrics at lengths 16 and 128, changing the length-128 ordering relative to DSpark-RNN. These results establish LangFlow's longer-sequence advantage under the evaluated budgets and highlight iterative full-sequence refinement as a route to improving standalone generation beyond gains from recurrent head memory.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.40847 — 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 2026-11-03T05:18:52.177Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).