Parallel Rollout Approximation Speeds Up Pixel-Space Autoregressive Image Training
June 25, 2026
Pixel-space autoregressive image generation suffers from train-inference gap and error accumulation across steps; exact rollout training fixes this but is too slow for practical use. Parallel Rollout Approximation generates low-dimensional intermediate states in parallel to approximate sequential rollout, addressing both high-dimensional patch error and the train-inference mismatch jointly.
HOW THIS AFFECTS YOU
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researcherOffers a scalable alternative to sequential rollout training for pixel-space AR models, potentially improving generation quality without the sequential compute cost.