Where the Growth of Peer Review Goes: A Decade of ICLR Discussions on OpenReview

Peer review in AI and machine learning conferences now runs at a size that no reviewing system was designed for. ICLR itself grew $40\times$ between 2017 and 2026 with its review process in public on OpenReview. In this paper, we use this public record to understand how each part of peer review scales with the size of a venue, what drives the growth, and what the discussion still tells us about the reviewers' judgment. Each part is summarized by a power law in the number of submissions, with the exponent showing who absorbs the growth. We find that authors absorbed most of it, as their rebuttals grew faster per paper than the reviews that they answer, while public attention did not grow at all. A small group of prolific authors holds a growing share of all submissions, which exhibits the property of cumulative advantage. We also find that the discussion became routine, with reviewer follow-ups that are frequent, short, and polite, and the review wording predicts scores somewhat less well than it did. These findings show where the load of a growing venue lands and which rules affect it.