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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
Authors: Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,505 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,877 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we report a preliminary numerical validation of our theoretical ο¬ndings. |
| Researcher Affiliation | Collaboration | Jean Tarbouriech Facebook AI Research Paris & Inria Lille EMAIL Matteo Pirotta Facebook AI Research Paris EMAIL Michal Valko Deep Mind Paris EMAIL Alessandro Lazaric Facebook AI Research Paris EMAIL |
| Pseudocode | Yes | Algorithm 1 GOSPRL Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | No | The paper mentions domains like 'River Swim domain' and 'Garnet environment' but does not provide concrete access information (link, DOI, or formal citation with author/year) for public availability. |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We consider a TREASURE-type problem (Sect. 4.1), where for all (s, a) we set b(s, a) = 10 instead of 1 (we call it the TREASURE-10 problem). |