Google’s Dream-RSI reduces discovery-agent calls by 162x

Google researchers have built a system that lets AI discovery agents get dramatically smarter without actually doing more work. Dream-RSI, a framework developed by teams at Google DeepMind, the University of Maryland, and the University of Virginia, cuts the number of required discovery-agent calls from 51,200 down to just 317 on a benchmark optimization task. That’s a roughly 162x reduction. The paper, published on arXiv as arXiv:2609.14858, describes a system that essentially teaches AI agents to replay and learn from their own prior searches rather than running new ones from scratch every time. How Dream-RSI actually works The framework operates on a three-stage loop, each building on the last. First, the system runs an initial phase of online exploration, generating what the researchers call “discovery trees.” These are structured records of every search path the agent took, including the dead ends and the breakthroughs. In the second stage, those historical trails get transformed into replay simulators. Instead of needing to call the underlying coding agent again, the system reconstructs the decision landscape from data it already has. The third stage is where the “dreaming” h...