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Aligning video generation models to human intent and physical constraints is significantly harder than for text or images, necessitating a diverse suite of post-training techniques beyond simple fine-tuning.
Video-FLAIR boosts accuracy by over 5 points while slashing token usage from 417 to just 95 by dynamically selecting reasoning strategies for multimodal queries.
