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..
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
Authors: Rong Zhu, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present extensive numerical results obtained from experiments performed in simulated cluster environments. In our experiments training time is reduced by 44% on average and learned policies generalize to multiple unseen circumstances. |
| Researcher Affiliation | Industry | Rong Zhu*, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou Alibaba Group |
| Pseudocode | Yes | Algorithm 1: Unified Synchronization Policy Framework |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for its methodology. |
| Open Datasets | Yes | In each instance, we randomly sample 50% data from the MNIST dataset and run the standard SGD for training. |
| Dataset Splits | No | The paper mentions "88% validation accuracy" as a termination criterion but does not specify the size or split methodology for a validation dataset. |
| Hardware Specification | No | The paper states, "We implement RLP in a simulated cluster/PS environment." As experiments are conducted in a simulated environment, no specific physical hardware specifications are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions using "standard off-the-shelf deep Q-learning algorithm" and "two-layer neural networks" but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hyper-parameters for RLP are set as follows: historical size H = 10, replay pool size N = 50, mini-batch size |B| = 32, copy rate c = 5, discount factor γ = 0.8, exploration probability ϵ = 0.1 and learning rate to be 0.01. For the underlying DNN model, we set its batch size to 16 and learning rate to 0.01. |