Unverified50% confidenceFactExact time
Compared to score-function-based diffusion models, the flow matching training objective is more concise, and the trajectory can be designed as a rectified flow, enabling high-quality generation in fewer inference steps.
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Long-term
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9/23/2026
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UnverifiedDiffusion models perform better in global consistency but have slower inference speeds, while autoregressive models generate content frame by frame or segment by segment.75% similarUnverifiedAn efficient inference engine can accelerate rollout generation, thereby shortening the iteration cycle of RL training.72% similarUnverifiedThe technical principle behind four-step generation typically comes from accelerated sampling paradigms such as Consistency Models or Flow Matching, which use distillation training to enable the model to accurately predict the denoising direction at sparse time steps.71% similarUnverifiedBehaviors learned by LARA can be loaded, removed, blended, or routed during inference while the model itself remains frozen.71% similar
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