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 theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
Authors: Tom Yan, Zachary Lipton
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 36,794 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,754 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning |
| Researcher Affiliation | Academia | Tom Yan, Zachary Lipton Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1 Hierarchical-UCB-VI (Hier-UCB-VI); Algorithm 2 Hierarchical-REGIME (Hier-REGIME) |
| Open Source Code | No | This is a theory paper that does not involve code. |
| Open Datasets | No | This is a theory paper that has no experiments section. |
| Dataset Splits | No | This is a theory paper that has no experiments section. |
| Hardware Specification | No | This is a theory paper that has no experiments section. |
| Software Dependencies | No | This is a theory paper that does not involve code. |
| Experiment Setup | No | This is a theory paper that has no experiments section. |