AI BriefingEfficient Rollout Scaling for Diffusion RL
Key point
NVIDIA Sol-RL reduces cost and accelerates convergence by separating FP4 rollouts from BF16 updates.
Details
NVIDIA's Sol-RL proposes a 2-stage framework that separates exploration from learning. First, it generates a large candidate set with FP4 rollouts, then performs policy updates only on selected samples using BF16.
This structure lowers the computational cost of the candidate generation stage while allowing updates to be focused only on important samples. As a result, it reduces compute cost while improving alignment and accelerating the convergence speed of diffusion model post-training.
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