Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Hierarchical Rectified Flow
Authors: Yichi Zhang, Yici Yan, Alex Schwing, Zhizhen Zhao
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify this on synthetic 1D and 2D data as well as MNIST, CIFAR-10, and Image Net-32 data. |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign |
| Pseudocode | Yes | Algorithm 1: Hierarchical Rectified Flow Training Algorithm 2: Hierarchical Rectified Flow Sampling |
| Open Source Code | Yes | Our code is available at: https://riccizz.github.io/HRF/. |
| Open Datasets | Yes | We empirically verify this on synthetic 1D and 2D data as well as MNIST, CIFAR-10, and Image Net-32 data. |
| Dataset Splits | Yes | For each dataset in the low-dimensional experiments, we use 100,000 data points for training and another 100,000 data points for evaluation. |
| Hardware Specification | Yes | We train all models on a single NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | In our experiments, we use the RK45 ODE solver (Dormand & Prince, 1980) provided by the scipy.integrate.solve_ivp package. We use atol = 1e-5 and rtol = 1e-5. |
| Experiment Setup | Yes | We use the Adam optimizer with β1 = 0.9, β2 = 0.999, and ϵ = 10^-8, with no weight decay. For MNIST, the U-Net has channel multipliers [1, 2, 2], while for CIFAR-10 and Image Net-32, the channel multipliers are [1, 2, 2, 2]. The learning rate is set to 1e-4 with a batch size 128 for MNIST and CIFAR-10. For Image Net-32, we increase the batch size to 512 and adjust the learning rate to 2e-4. We train all models on a single NVIDIA RTX A6000 GPU. For MNIST, we train both the baseline and our model for 150,000 steps while we use 400,000 steps for CIFAR-10. |