NVIDIA’s γ‑World is a generative world model that can simulate multiple agents simultaneously, using Simplex Rotary Agent Encoding for permutation‑symmetric control and Sparse Hub Attention to keep cross‑agent computation linear. The model produces real‑time, 24 FPS rollouts and can zero‑shot generalize from two‑player to four‑player scenarios, improving video fidelity and inter‑agent consistency.
Spectral Guidance leverages the intrinsic geometry of diffusion processes to project arbitrary conditioning signals, labels, CLIP embeddings, masks, onto the sampling trajectory without retraining. The method boosts conditional accuracy on CIFAR‑10 by 37 points over the strongest training‑free baseline while cutting sampling time by four‑fold, and reveals an optimal guidance window.
The paper shows RL fine‑tuning retains prior capabilities by preserving internal computational circuits, unlike supervised fine‑tuning which adapts faster but disrupts circuits and causes forgetting. Using a metric called differential circuit vulnerability on Qwen2.5‑3B‑Instruct for scientific QA, they demonstrate RL’s mechanistic advantage.