Google, DeepMind, Maryland & Virginia · Recursive Self-Improvement

Dream-RSI

Recursive self-improvement through evolving worlds. A coding agent explores and builds a discovery tree, that tree is then reused as a simulator, and thousands of candidate policies get evaluated against the simulator at zero executions before the best one is redeployed. Reported results include 2.43x fewer generations on VGG16 at comparable performance, 2.09x higher scores on ConvDiv at comparable budget, and 162x fewer discovery-agent calls than the baseline SimpleTES on Lasso tasks, with experiments run on Gemini 3.1-Pro and 3.7-Flash across algorithm engineering, mathematical optimisation and GPU kernel work. Code released.