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..
Understanding the Effect of Stochasticity in Policy Optimization
Authors: Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvari, Dale Schuurmans
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Researcher Affiliation | Collaboration | Jincheng Mei 1 3 Bo Dai 3 Chenjun Xiao 1 3 Csaba Szepesvári 2 1 Dale Schuurmans 3 1 1University of Alberta 2Deep Mind 3Google Research, Brain Team equal advising |
| Pseudocode | No | The paper defines update rules (e.g., Update 1, Update 2) mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, as indicated by the N/A responses in the 'If you ran experiments' section of the self-assessment. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with dataset splits, as indicated by the N/A responses in the 'If you ran experiments' section of the self-assessment. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications, as indicated by the N/A responses in the 'If you ran experiments' section of the self-assessment. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers, as indicated by the N/A responses in the 'If you ran experiments' section of the self-assessment. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameters or training configurations, as indicated by the N/A responses in the 'If you ran experiments' section of the self-assessment. |