Generalization and Distributed Learning of GFlowNets
Authors: Tiago Silva, Amauri Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with synthetic and real-world problems demonstrate the benefits of SAL over centralized training in terms of mode coverage and distribution matching. |
| Researcher Affiliation | Collaboration | Tiago da Silva Getulio Vargas Foundation EMAIL Amauri Souza Federal Institute of Cear a EMAIL Omar Rivasplata University of Manchester EMAIL Vikas Garg Yai Yai Ltd and Aalto University EMAIL Samuel Kaski Aalto University, University of Manchester EMAIL Diego Mesquita Getulio Vargas Foundation EMAIL |
| Pseudocode | Yes | Algorithm 1 Subgraph Asynchronous Learning |
| Open Source Code | No | The paper does not explicitly state that code for the methodology described in this paper is available or provide a link to a repository. While it acknowledges another paper's code being public, it does not do so for its own. |
| Open Datasets | Yes | 1. Hypergrid (Bengio et al., 2021; Malkin et al., 2022; 2023; Pan et al., 2023b; Krichel et al., 2024). 2. SIX6 (Jain et al., 2022; Malkin et al., 2022; Shen et al., 2023; Chen & Mauch, 2024; Kim et al., 2024a). ... Barrera et al., 2016; Trabucco et al., 2022. 3. PHO4 (Jain et al., 2022; Malkin et al., 2022; Shen et al., 2023; Chen et al., 2023). ... Barrera et al., 2016; Trabucco et al., 2022. |
| Dataset Splits | Yes | For this, we disjointly partition the dataset Tn with n = 3 104 into sets Tα and T1 α with α = 0.6. |
| Hardware Specification | Yes | All experiments were conducted on a single Linux machine with 128 GB of RAM and featuring a NVIDIA RTX 3090 GPU and 12th Gen Intel(R) Core(TM) i9-12900K CPU. Unless specified otherwise, the code for reproducing the experiments below was executed on this GPU. |
| Software Dependencies | No | The paper mentions optimizers like SGD and Adam, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in their implementation. |
| Experiment Setup | Yes | For the experiments in Figure 1, we considered the set generation task (see Appendix A) with W {32, 64} elements to choose from and set size S = 6, and the forward policy was parameterized by an MLP with 2 64-dimensional layers. The elements log-utilities u were sampled from [ 1, 1] prior to training and the resulting values were normalized so that the largest reward of a set was 5. For both settings in Figure 1, the models were trained for 1500 epochs with a batch size of 128. |