Dream-RSI paper: improving search strategy alone cuts AI discovery compute up to 162x

r0ck3t23 · x · 2026-09-19

Researchers from Google, Google DeepMind, the University of Maryland and the University of Virginia present Dream-RSI, a way to make AI systems stronger without touching model weights.

The model and evaluator stay fixed; what improves is the search strategy — where to explore, how many branches to run in parallel, when to abandon or invest more.
Agents leave behind full search trees (wins, dead ends, scores, costs). Dream-RSI turns that history into a training ground: it replays the search and cheaply tests many alternative exploration strategies against recorded outcomes, without rerunning expensive experiments. The best strategy then runs on the real task, generating a new tree, and the loop repeats. The authors frame it as the inverse of systems like AlphaEvolve: improving the search itself rather than the candidate solutions.

Reported gains:

Lasso path solving: up to 162x fewer discovery agent calls than SimpleTES, 1.7x fewer than a strong fixed baseline.
Mathematical optimization: over 50x the budget efficiency of SimpleTES.
GPU kernels: similar performance with 1.8–2.4x fewer generations, or faster kernels at equal budget.

The takeaway: capability progress isn't only about better weights — improving how efficiently a system searches the capability it already has can unlock a lot of it.

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Dream-RSI paper: improving search strategy alone cuts AI discovery compute up to 162x

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