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..
On Stationary Point Convergence of PPO-Clip
Authors: Ruinan Jin, Shuai Li, Baoxiang Wang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we provide a comprehensive analysis that shows the stationary point convergence of PPO-Clip and the convergence rate thereof. |
| Researcher Affiliation | Academia | Ruinan Jin The Chinese University of Hong Kong, Shenzhen EMAIL Shuai Li Shanghai Jiao Tong University EMAIL Baoxiang Wang The Chinese University of Hong Kong, Shenzhen EMAIL |
| Pseudocode | No | The paper does not include any structured pseudocode or algorithm blocks. |
| 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 is theoretical and does not mention the use or availability of a dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve experiments requiring hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup. |