Scaling Video Generation for Reasoning: At What Cost?

arXiv:2609.36599 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/NVIDIA/exemplar-performance

Terminology

• Accuracy — 89× in this paper · explained in 1 episode
• Transformer — 25× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Scaling Laws — 5× in this paper · explained in 4 episodes
• World Model — 5× in this paper · explained in 7 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 7 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• ARK — 1× in this paper · explained in 1 episode
• Action-Conditioned World Model — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
• LTX-Video: Realtime Video Latent Diffusion
• Deep Learning Scaling is Predictable, Empirically
• Training Compute-Optimal Large Language Models
• Scaling Laws for Neural Language Models
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Cosmos World Foundation Model Platform for Physical AI
• World Simulation with Video Foundation Models for Physical AI
• Can Video World Models Track Unobserved World States?
• RoFormer: Enhanced Transformer with Rotary Position Embedding
• Wan: Open and Advanced Large-Scale Video Generative Models
• A Very Big Video Reasoning Suite
• VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
• World Action Models are Zero-shot Policies
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• MBench: A Comprehensive Benchmark on Memory Capability for Video World Models

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