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..
Making RL with Preference-based Feedback Efficient via Randomization
Authors: Runzhe Wu, Wen Sun
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Overall, while our main contribution is on the theoretical side, our theoretical investigation provides several new practical insights. |
| Researcher Affiliation | Academia | Runzhe Wu Department of Computer Science Cornell University EMAIL Wen Sun Department of Computer Science Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Preference-based and Randomized Least-Squares Value Iteration (PR-LSVI) |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical algorithms and does not perform experiments on specific datasets. Therefore, no information about publicly available or open datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments with dataset splits. No specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe the execution of experiments requiring specific hardware. Therefore, no hardware specifications (e.g., GPU/CPU models, memory details) are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis rather than practical implementation details. No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) are mentioned. |
| Experiment Setup | No | The paper is theoretical and defines algorithm parameters (e.g., sigma_r, sigma_P, epsilon) as part of the theoretical framework rather than specific experimental setup details for empirical evaluation. No section titled 'Experimental Setup' or similar detailing training configurations or system-level settings for experiments is present. |