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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.
Simply modifying the *concrete* words in your VLM training data can unlock surprisingly large gains in compositional reasoning.
