weight-space-learning

A collection of weight space learning including papers, codes, and datasets.

Official PyTorch Implementation for the "Recovering the Pre-Fine-Tuning Weights of Generative Models" paper (ICML 2024).

Official PyTorch Implementation for the "Unsupervised Model Tree Heritage Recovery" paper (ICLR 2025).

Awesome papers on weight-space learning

Code Repository for the ICML 2024 paper: "Towards Scalable and Versatile Weight Space Learning".

Code Repository for the NeurIPS 2022 paper: "Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights".

Official PyTorch Implementation for the "Learning on Model Weights using Tree Experts" paper (CVPR 2025).

An official implementation of ProbeGen

Code Repository for the CVPR 2026 paper: "Learning Geospatial Representations from Models, not Data".

AAAI'26 Oral: "WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network"

Official implementation of Weight-Space Linear Recurrent Neural Networks (ICLR 2026)

ICML'26: Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion

[ICLR 2026] Weight Space Representation Learning on Diverse NeRF Architectures

Website for the ICLR 2025 Weight Space Learning workshop.

SANE backbone trained on multiple model zoos.

Measuring how much of the INR weight-space perception gap is parameter symmetry. Exact D∞≀Sₙ characterization for sine networks, an orbit-only intervention that isolates what the group alone costs, and a phasor-graded reader placing 3rd/3rd/2nd on the standard INR benchmarks. Pre-registered; every prediction scored.