Recurrent Diffusion for Parameter Generation (RPG) enables efficient creation of full neural network parameters at unprecedented scale, overcoming previous limitations.

• Partitions network parameters into non-overlapping tokens for efficient processing
• Employs a recurrent mechanism to learn parameter generation at scale
• Achieves generation of hundreds of millions of parameters on a single GPU
• Addresses key engineering challenges in scaling parameter generation for modern AI systems

This breakthrough has significant implications for AI engineering, enabling more accessible development of large models without requiring extensive computational resources.

Recurrent Diffusion for Large-Scale Parameter Generation