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..
SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
Authors: Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, Craig Boutilier
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Empirical Evaluation: Simulation, 6 Empirical Evaluation: Live Experiments |
| Researcher Affiliation | Collaboration | 1Google Research 2Department of Computer Science, University of Texas at Austin |
| Pseudocode | No | The paper presents mathematical equations and descriptions of updates, but it does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that TensorFlow software is available, but it does not provide an explicit statement or link for the open-source code of the methodology described in this paper. |
| Open Datasets | No | We construct a simulation environment since most public datasets are point-wise, static, and not designed for evaluating multi-step user-recommender interactions. |
| Dataset Splits | No | The paper describes evaluating strategies on 5000 simulated users but does not specify explicit train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper mentions training on 'large-scale recommenders' and using 'distributed training', but it does not provide specific details about the hardware used, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper states that 'The model is trained using Tensor Flow', but it does not specify the version number of TensorFlow or other software dependencies. |
| Experiment Setup | Yes | The paper specifies parameters for the simulation environment, such as '|T| = 20, m = 10, k = 3', and describes the training approach in live experiments: 'We train on-policy over pairs of consecutive start page visits, with LTV labels computed using Eq. (14), and use top-k optimization for both training and serving'. |