Insights from the ICLR Peer Review and Rebuttal Process

TL;DR

This paper provides a large-scale analysis of the ICLR peer review process, revealing how initial scores, reviewer interactions, and rebuttals influence review outcomes, with implications for improving fairness and efficiency.

Contribution

It introduces a comprehensive analysis combining quantitative data and LLM-based review categorization to understand review dynamics and identify factors affecting score changes.

Findings

Initial scores strongly predict score changes

Rebuttals can significantly influence borderline papers

Reviewer co-rating patterns impact review outcomes

Abstract

Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and dynamics of the review process is crucial for improving its efficiency, effectiveness, and the quality of published papers. We present a large-scale analysis of the ICLR 2024 and 2025 peer review processes, focusing on before- and after-rebuttal scores and reviewer-author interactions. We examine review scores, author-reviewer engagement, temporal patterns in review submissions, and co-reviewer influence effects. Combining quantitative analyses with LLM-based categorization of review texts and rebuttal discussions, we identify common strengths and weaknesses for each rating group, as well as trends in rebuttal strategies that are most strongly associated with score changes. Our findings show that initial scores and the ratings of co-reviewers are the strongest predictors of score changes during the rebuttal, pointing to a degree of reviewer influence. Rebuttals play a valuable role in improving outcomes for borderline papers, where thoughtful author responses can meaningfully shift reviewer perspectives. More broadly, our study offers evidence-based insights to improve the peer review process, guiding authors on effective rebuttal strategies and helping the community design fairer and more efficient review processes. Our code and score changes data are available at https://github.com/papercopilot/iclr-insights.