Dream-RSI rewrites a coding agent's exploration policy

In a large search space, a fixed exploration strategy stops adapting as the space grows, while tuning the strategy online consumes a full discovery run for every policy test. Dream-RSI treats a coding agent's history, the record of what it tried and what happened, as a replay simulator. A candidate exploration policy can be tested by re-walking that recorded tree in a different order instead of re-running the agent, which the authors call "dreaming." The method leaves the underlying coding agent unchanged and rewrites the orchestration layer that decides what to explore next. In algorithm engineering on Gemini 3.1 Pro, the paper's code repository reports 1.22x faster downstream runtime and 1.74x less discovery compute than Recursive Fixed Exploration, plus 162x fewer calls than SimpleTES. In GPU kernel engineering, 4 of 4 kernels improved at 2.09x higher performance for equal budget. In mathematical optimization, 2 of 3 tasks matched or beat the baseline. The Hacker News discussion (207 points, 50 comments a day after posting) split over whether "RSI" is the right word for it. Commenter bob1029 called the exploration-policy optimization "not recursively intelligent in any way," and mohamedmohey argued "if this is RSI then all RL is RSI." Co-author xidong_wu replied in the thread to confirm the mechanism: "This paper optimizes a controller/policy which will be used to agent itself in the next round." Full reproduction code and the discovered programs are not released yet; the repository lists them as still being prepared.

Why it matters: Dream-RSI offers a concrete harness pattern: record exploration decisions, replay them offline to evaluate policy changes, and deploy the best-performing policy. The method reduces the cost of iterating on how an agent chooses its next action.