Reimagining Parameter Space Exploration with Diffusion Models

Lijun Zhang; Xiao Liu; Hui Guan

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arXiv · 2506.17807

Reimagining Parameter Space Exploration with Diffusion Models

Lijun Zhang·Xiao Liu·Hui Guan

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Abstract

Adapting neural networks to new tasks typically requires task-specific fine-tuning, which is time-consuming and reliant on labeled data. We explore a generative alternative that produces task-specific parameters directly from task identity, eliminating the need for task-specific training. To this end, we propose using diffusion models to learn the underlying structure of effective task-specific parameter space and synthesize parameters on demand. Once trained, the task-conditioned diffusion model can generate specialized weights directly from task identifiers. We evaluate this approach across three scenarios: generating parameters for a single seen task, for multiple seen tasks, and for entirely unseen tasks. Experiments show that diffusion models can generate accurate task-specific parameters and support multi-task interpolation when parameter subspaces are well-structured, but fail to generalize to unseen tasks, highlighting both the potential and limitations of this generative solution.

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Lijun Zhang, Xiao Liu, Hui Guan. 2025-06-21. Reimagining Parameter Space Exploration with Diffusion Models. https://arxiv.org/abs/2506.17807

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