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.
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Authors: Gen Li, Laixi Shi, Yuxin Chen, Yuantao Gu, Yuejie Chi
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,995 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,708 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 | The proof of this theorem can be found in the full version Li et al. (2021c). Additionally, the paper's own checklist states under '3. If you ran experiments...' that all sub-questions (a, b, c, d) are '[N/A]', indicating no empirical experiments were conducted. |
| Researcher Affiliation | Academia | Gen Li Princeton Laixi Shi CMU Yuxin Chen Princeton Yuantao Gu Tsinghua Yuejie Chi CMU Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. |
| Pseudocode | Yes | Algorithm 1: Q-Early Settled-Advantage; Algorithm 2: Auxiliary functions |
| Open Source Code | No | The paper states under '3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]'. |
| Open Datasets | No | The paper is theoretical and does not describe training on any specific dataset. It states under '3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]'. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for validation. It states under '3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for experiments. It states under '3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]'. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies or versions. It states under '3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]'. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details like hyperparameters or training configurations for empirical runs. It states under '3. If you ran experiments... (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |