Valeo Study: Scaling Laws for Video Diffusion on 5,500 Hours of Driving Data
andrew_n_carr · x · 2026-09-01
Valeo AI releases VATIX, analyzing scaling laws for video diffusion on a fixed driving dataset (5,500 hours, 28 countries).
- Setup: Optimizes model size (N) and training exposure (D) under compute budget (C) to minimize validation loss.
- Key Finding: Training scene exposure reduces loss much faster than parameters (αD ≈ 0.74 vs αN ≈ 0.21), suggesting "seeing more" beats "getting bigger" in data-constrained scenarios.
- Prediction: Laws fitted on 1.1B params predicted 9B model performance with only 3.6% error.
- SOTA: Achieved new state-of-the-art on nuScenes Vista.
Related event: Valeo Derives Video Diffusion Scaling Laws from 5,500 Hours of Driving Data(3 posts)→
More from Research
- Kornia releases open-source Rust visual-inertial SLAM pipeline kornia-slam v0.1 — edgarriba · 2026-10-16
- Interpretability debate: one interpretable polysemantic neuron doesn't prove all are — aran_nayebi · 2026-10-16
- Optuna creator Akiba: when 2 hyperparameters cost $20M, what's left for BO/HPO — Stefania_druga · 2026-10-16
- AI will be superhuman at science regardless, and Europe can't opt out — teortaxesTex · 2026-10-16
- SERO augments long-horizon agentic RL with executable oracle continuations — Shanghai-AI-Laboratory · 2026-10-16
- Tsinghua and Amap propose OmniVerifier-C1, a graded verifier beating binary judges — Bowei Liu · 2026-10-16