AI models are becoming exponentially better at the experimental research process itself (not just raw capability), with frontier models now reaching expert-level results using 2.3x less compute—a skill that could significantly accelerate AI R&D timelines.
TasteVal is a benchmark measuring how efficiently AI models design and conduct experiments to solve research problems. Rather than evaluating raw problem-solving ability, it measures 'experimental taste'—the skill to iteratively design good experiments and interpret results—by comparing how much compute a model needs versus human experts to reach the same performance level.