Reviser: Revision-Capable Text Generation via Autoregressive Cursor Actions

Abstract:Revision-capable generation is appealing because it can insert or revise earlier content, but many non-autoregressive and edit-based approaches obtain this flexibility through repeated sequence-level computation. We propose Reviser, a decoder-only Transformer that generates a response as a sequence of cursor-relative actions on a mutable canvas. At each step, Reviser predicts exactly one action token: INSERT(token), MOVE($\Delta$), or STOP, and is autoregressive over edit-history actions rather than final text order. This design enables genuinely non-monotonic generation while preserving a simple next-action interface. On a continuation benchmark, Reviser is strongly preferred to SEDD and MDLM in our arena evaluations, and trajectory statistics confirm that the model performs frequent backward moves and mid-canvas insertions rather than merely emulating end-append decoding. Against size-matched autoregressive baselines, Reviser is competitive at both the 100M and 300M scales. Under our shared FLOPs convention, Reviser also requires substantially less inference compute than representative multi-pass refinement and diffusion-style baselines.

45 pages, 2 figures. Code: this https URL Checkpoints: this https URL

Computation and Language (cs.CL)

arXiv:2609.20830 [cs.CL]

(or arXiv:2609.20830v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.20830 Focus to learn more arXiv-issued DOI via DataCite

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