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 Understanding and Improving GFlowNet Training
Authors: Max W Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on biochemical design tasks, we demonstrate that these changes in learned flows can significantly impact sample efficiency and convergence to the target distribution, with up to 10 improvement. |
| Researcher Affiliation | Collaboration | 1Genentech, South San Francisco, USA 2Prescient Design, Genentech, South San Francisco, USA 3Recursion Pharmaceuticals, Salt Lake City, Utah 4Department of Computer Science, New York University, New York, USA. |
| Pseudocode | No | The paper describes methods in text and equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/maxwshen/gflownet. |
| Open Datasets | Yes | SIX6 (TFBind8)... from (LA et al., 2016; Trabucco et al., 2022). |
| Dataset Splits | No | The paper describes a generative model that samples data during training and evaluation, and does not specify traditional train/validation/test dataset splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Py Torch neural network initializations' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We found it useful to clip gradient norms to a maximum of 10.0. We also clipped policy logit predictions to a minimum of -50.0 and a maximum of 50.0. We initialized log ZĪø to 5.0... every active training round we sampled a batch of 16 x... For prioritized replay training, we focus on the top 10% ranked by reward and randomly sample among them to be 50% of the batch... We use a small neural net policy with two layers of 16 hidden units. We use an exploration epsilon of 0.10. |