An open benchmark that evaluates AI agents on competition-level machine learning research tasks to measure how well they can accelerate ML research.
Endorsements made here support Algorithmic Research Group.
An open benchmark that evaluates AI agents on competition-level machine learning research tasks to measure how well they can accelerate ML research.
Endorsements made here support Algorithmic Research Group.
Project Details
Updated 05/18/26 · By grantmaking.aiThe ML Research Benchmark (MLRB) is a suite of seven competition-level tasks derived from recent machine learning conference tracks. It focuses on activities central to ML research—including pretraining, finetuning, model compression, model merging, and efficiency-focused training—and is paired with baseline agents and evaluation tooling so that different AI systems can be compared on realistic research workflows.
Theory of Impact
Updated 05/18/26 · By grantmaking.aiMLRB aims to make progress in AI safety and governance by giving researchers a realistic, competition-style benchmark for measuring how well AI agents can carry out end-to-end machine learning research. By grounding evaluation in concrete research tasks and providing open-source baselines and tooling, the project helps identify where current agents fall short, track improvements over time, and inform decisions about the risks and responsibilities of deploying more capable research agents.
People
Updated 05/18/26 · By grantmaking.aiCreator and lead researcher
Discussion
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