1,200 ICLR 2026 papers with code made r/MachineLearning ask what reproducibility means

r/MachineLearning treated the ICLR 2026 code/data thread as useful, but not as a victory lap. The post shared a list of about 1,200 accepted ICLR 2026 papers with public code, data, or demo links, saying the links were extracted from paper submissions and represented roughly 22% of more than 5,300 accepted papers. The community’s first move was to ask what that number really proves.

Paper Digest’s index says ICLR 2026 starts in Rio de Janeiro on April 22, 2026, and frames the list as a way to help readers engage quickly with accepted research. It also includes an important caveat: the index was generated through automated extraction, some public resources may have been missed, and some repositories may not become fully public until the conference begins.

That caveat became the thread’s center. One commenter said their own accepted paper had public code and full reproducibility but was missing from the list. Another opened a random item and hit a GitHub 404. A third asked the harder question: among the 1,200 repositories, how many actually reproduce the paper’s results, and how many run without issues? In other words, “contains a link” is not the same as “reproducible.”

The useful signal is not cynicism about ICLR. It is a more mature definition of open research. A link is one layer. License clarity, dependency pinning, data access, seeds, training cost, evaluation scripts, checkpoints, and maintenance all matter if another lab is meant to rerun the work. The 1,200-paper number suggests real progress toward openness, but the r/MachineLearning response adds the missing pressure: public code should be treated as the beginning of the reproducibility audit, not the end.