Recurrent Diffusion for Large-Scale Parameter Generation (2501.11587v2)

Abstract: Parameter generation has long struggled to match the scale of today large vision and LLMs, curbing its broader utility. In this paper, we introduce Recurrent Diffusion for Large Scale Parameter Generation (RPG), a novel framework that generates full neural network parameters up to hundreds of millions on a single GPU. Our approach first partitions a networks parameters into non-overlapping tokens, each corresponding to a distinct portion of the model. A recurrent mechanism then learns the inter token relationships, producing prototypes which serve as conditions for a diffusion process that ultimately synthesizes the full parameters. Across a spectrum of architectures and tasks including ResNets, ConvNeXts and ViTs on ImageNet 1K and COCO, and even LoRA based LLMs RPG achieves performance on par with fully trained networks while avoiding excessive memory overhead. Notably, it generalizes beyond its training set to generate valid parameters for previously unseen tasks, highlighting its flexibility in dynamic and open ended scenarios. By overcoming the longstanding memory and scalability barriers, RPG serves as a critical advance in AI generating AI, potentially enabling efficient weight generation at scales previously deemed infeasible.

• The paper introduces the Recurrent Diffusion framework that combines recurrent modeling and diffusion to efficiently generate large-scale neural network parameters.
• It partitions network parameters into tokens and uses a recurrent model to capture inter-token correlations for improved parameter synthesis.
• Empirical results demonstrate that RPG-generated models achieve robust performance on ImageNet, semantic segmentation, and object detection tasks.

Recurrent Diffusion for Large-Scale Parameter Generation

The paper introduces an innovative approach called Recurrent Diffusion for large-scale Parameter Generation (RPG), designed to address the scaling challenges inherent in neural network parameter generation. The fundamental dilemma tackled by this research is the vast scale gap between current vision and LLMs compared to generated parameters, a discrepancy at least of the order of $10^{4}$. The RPG framework seeks to mitigate these challenges using a combination of recurrent modeling and diffusion processes, enabling efficient parameter generation across diverse network architectures including ConvNeXt-L and LLaMA-7B using a single GPU.

Methodology Overview

The RPG methodology begins by partitioning the trained network parameters into distinct, non-overlapping segments termed as tokens. This division takes into account the layer-wise distribution heterogeneity, normalizing parameters by their mean and standard deviation within each layer. The tokens thus created serve as elemental units of information fed into a recurrent model designed to elucidate inter-token correlations analogous to token relationship modeling in LLMs or patch relations in vision transformers.

The recurrent model produces prototypes for parameter generation, which are subsequently employed as conditions in a diffusion process to synthesize network parameters. Notably, RPG eschews convolution over images or sequences, focusing instead on convolution over parameter spaces, thereby fundamentally redefining parameter correlation modeling.

Performance and Empirical Results

Experimental validation demonstrates RPG's capacity to generate model parameters that reliably replicate the performance of their original counterparts across an array of tasks. On classification tasks using ImageNet-1K, the generated models achieved metrics nearly equivalent to those of trained networks, a feat accomplished without computational prohibitive resource use. Importantly, beyond merely replicating performance, RPG-generated parameters exhibit robustness to unseen task configurations, markedly extending its practical utility.

The results on semantic segmentation, object detection, and commonsense reasoning further corroborate RPG's versatility and effectiveness. For example, on COCO and ADE20K datasets, the generated parameters not only maintained fidelity to trained models but occasionally surpassed them in specific performance measures.

Theoretical Insights and Future Directions

RPG introduces a significant theoretical advancement by demonstrating that large-scale neural network parameter generation can be effectively modeled analogous to segment-based recurrent sequence prediction tasks. This analogy underscores the potential for broader applications of RPG in diverse fields where parameter generation bottlenecks currently exist.

Future research could benefit from exploring RPG's adaptability to novel architectures or hybridizing RPG with other generative paradigms to enhance adaptability and performance across even more complex network topologies. This approach also lays a groundwork for potentially realizing AI that self-generates optimized network architectures tailored to specific tasks.

In summary, the RPG framework asserts a new methodological direction in parameter generation, leveraging recurrent diffusion to surmount scalability challenges and promising substantial expansions in both the scalability and accessibility of generative neural network methodologies. Through rigorous validation and thoughtful architecture, RPG stands as a critical addition to the corpus of generative model research, warranting further exploration and adaptation.