Reimagining Parameter Space Exploration with Diffusion Models

• URL: http://arxiv.org/abs/2506.17807v1
• Date: Sat, 21 Jun 2025 20:30:17 GMT
• Title: Reimagining Parameter Space Exploration with Diffusion Models
• Authors: Lijun Zhang, Xiao Liu, Hui Guan,
• Abstract summary: Adapting neural networks to new tasks typically requires task-specific fine-tuning.<n>We explore a generative alternative that produces task-specific parameters directly from task identity.
• Score: 21.180663546314122
• License: http://creativecommons.org/licenses/by/4.0/
• 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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