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.
The Role of Coverage in Online Reinforcement Learning
Authors: Tengyang Xie, Dylan J Foster, Yu Bai, Nan Jiang, Sham M. Kakade
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 45,449 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,241 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 | While our results primarily concern analysis of existing algorithms rather than algorithm design, they highlight a number of exciting directions for future research, and we are optimistic that the notion of coverability can guide the design of practical algorithms going forward. |
| Researcher Affiliation | Collaboration | Tengyang Xie UIUC EMAIL Dylan J. Foster Microsoft Research EMAIL Yu Bai Salesforce Research EMAIL Nan Jiang UIUC EMAIL Sham M. Kakade Harvard University EMAIL |
| Pseudocode | Yes | Algorithm 1 GOLF (Jin et al., 2021a) input: Function class F, confidence width β >0. [...] Algorithm 2 Reward-Free Exploration with GOLF [...] Algorithm 3 Offline GOLF with Exploration Data and Target Reward |
| Open Source Code | No | The paper does not provide any statement about making its code open-source or provide a link to a code repository. |
| Open Datasets | No | As a theoretical paper focused on analysis and proofs, it does not conduct experiments on datasets, thus no training data is mentioned. |
| Dataset Splits | No | As a theoretical paper focused on analysis and proofs, it does not describe experimental validation on data, thus no dataset splits are provided. |
| Hardware Specification | No | As a theoretical paper, it does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | As a theoretical paper, it does not describe any experimental setup or specific software dependencies with version numbers. |
| Experiment Setup | No | As a theoretical paper, it does not describe an experimental setup with hyperparameters or training settings. |