DARL: Efficient Document-to-Markup Generation via Look-Ahead Diffusion Trajectory Sampling

While autoregressive (AR) Multimodal Large Language Mod-els (MLLMs) excel at complex document-to-markup generation, theirsequential decoding causes severe latency. Conversely, existing paralleland diffusion-based methods accelerate inference but often struggle tomaintain strict structural dependencies, resulting in significantly higherparsing errors. To bridge this gap, we propose DARL (Diffusion AutoRe-gression with Look-ahead), a novel hybrid decoding framework. DARLintroduces a Sliding Diffusion Block (SDB) that maintains a verifiedAR prefix for strict syntactic correctness while speculatively generat-ing a localized window of future tokens in parallel. To optimize thesediscrete diffusion trajectories, we introduce Online Monte Carlo Tra-jectory Generation (OMTG) and Diffusion Trajectory Preference Opti-mization (DTPO). OMTG dynamically samples candidate paths basedon the model’s real-time state, effectively mitigating the exposure biasinherent in static heuristics. DTPO integrates immediate and look-aheadrewards to optimize current accuracy and facilitate future correctness,ensuring stable and rapid convergence of the predictive trajectory space.Experiments on OmniDocBench-1.5 and olmOCR-Bench demonstratethat DARL establishes a new Pareto frontier in document parsing. Itachieves up to a 2.3× speedup while matching or exceeding the state-of-the-art accuracy of AR baselines. The code and model are available athttps://github.com/SCUT-DLVCLab/DARL.