Reproduce “ReproBridge: Building Reproducibility into ML Research Artifacts” on OpenResearch
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About this paper
Yubo Wang, Rui Meng, Jaehyun Nam, Tomas Pfister, Jinsung Yoon
ReproBridge packages recovered machine-learning experiment procedures and decisions so a fresh coding agent can reuse them without the construction conversation. Its artifacts combine refined code, environment setup, launchers, and a guide explaining settings and rationale. Across four models and three evaluations, success rose from 76.4% to 92.6%, while downstream API spending fell 19.7%, across 1,029 selected tasks.
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