Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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. |