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..
Order-Preserving GFlowNets
Authors: Yihang Chen, Lukas Mauch
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically prove that the training process of OP-GFNs gradually sparsifies the learned reward landscape in single-objective maximization tasks. The sparsification concentrates on candidates of a higher hierarchy in the ordering, ensuring exploration at the beginning and exploitation towards the end of the training. We demonstrate OP-GFN s state-of-the-art performance in single-objective maximization (totally ordered) and multi-objective Pareto front approximation (partially ordered) tasks, including synthetic datasets, molecule generation, and neural architecture search. |
| Researcher Affiliation | Collaboration | Yihang Chen Section of Communication Systems EPFL, Switzerland EMAIL Lukas Mauch Sony Europe B.V. Stuttgart Laboratory 1, Germany EMAIL |
| Pseudocode | Yes | The full pseudo algorithm is summarized in Algorithm 1. |
| Open Source Code | Yes | Our codes are available at https://github.com/yhangchen/OP-GFN. |
| Open Datasets | Yes | We empirically evaluate our method on synthesis environment Hyper Grid (Bengio et al., 2021a), and two real-world applications: NATS-Bench (Dong et al., 2021), and molecular designs (Shen et al., 2023; Jain et al., 2023) to demonstrate its advantages in the diversity and the top reward (or the closeness to the Pareto front) of the generated candidates. |
| Dataset Splits | Yes | We study the neural architecture search environment NATS-Bench (Dong et al., 2021), which includes three datasets: CIFAR10, CIFAR-100 and Image Net-16-120. ... Following Dong et al. (2021), when training the GFlow Net, we use the test accuracy at epoch 12 (u12( )) as the objective function in training; when evaluating the candidates, we use the test accuracy at epoch 200 (u200( )) as the objective function in testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only mentioning 'Estimated wall-clock time' in some contexts. |
| Software Dependencies | No | The paper mentions using 'torchgfn (Lahlou et al., 2023)' and 'Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for these or any other software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use Adam optimizer with a learning rate of 0.1 for Zθ s parameters and a learning rate of 0.001 for the neural network s parameters. ... We use an exploration epsilon εF = 0.10. ... clip the gradient norm to a maximum of 10.0, and the policy logit to a maximum of absolute value of 50.0. ... We initialize log Zθ to be 5.0. |