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Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steerable, low-dimensional...
ADOPD 2026 is presented, a reasoning-oriented extension of ADOPD that turns page decomposition into spatially grounded document understanding and provides a task framework that moves document understanding beyond localization toward anchor-grounded document intelligence.
This work proposes reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field and provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases.
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