DeepMind's Raila: AI Agents Self-Improve Fast but Plateau, Humans Keep Leaping
nathanbenaich · x · 2026-07-22
In a recent talk, Google DeepMind's Roberta Raila explored the current landscape of AI agents in scientific research. She noted that while AI agents possess self-improving capabilities, they quickly hit performance plateaus, whereas human scientists continue to make conceptual leaps.
To bridge this gap, she proposed a path forward leveraging reinforcement learning (RL) to discover "Move 37" moments in science, employing evolutionary search strategies rewarded for novelty, and utilizing DiscoBench, a new benchmark comprising over 400 million research tasks.
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