Towards Understanding and Improving GFlowNet Training
Authors: Max W Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 Zθ 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. |