Reimagining Parameter Space Exploration with Diffusion Models

2025-06-21

AI Summary

This work addresses the dependency of task-specific neural network fine-tuning on labeled task data. We propose a diffusion-based parameter generation paradigm that directly synthesizes network weights from task identifiers—such as textual descriptions or task IDs—thereby eliminating the need for task-specific annotations. Our core contribution is the first application of diffusion models to explicitly model the neural network parameter space, establishing a task-conditional generative framework that learns the mapping from task identifiers to distributions over parameters. The method enables high-fidelity reconstruction of parameters for seen tasks and supports smooth, continuous interpolation across multiple tasks in parameter space. Experiments demonstrate accurate parameter generation and stable interpolation for seen tasks; however, generalization to unseen tasks remains limited, exposing a fundamental challenge regarding the transferability and structure of neural parameter spaces. This reveals new insights into the geometric and statistical properties governing parameter-space generalization.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Generating task-specific neural network parameters without fine-tuning
• Exploring diffusion models for parameter space synthesis
• Assessing generalization to seen and unseen tasks

Innovation

• Diffusion models generate task-specific parameters directly
• Eliminates need for task-specific training
• Supports multi-task interpolation in structured subspaces