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Meta-exploration in long-horizon agent discovery is too expensive. Dream-RSI solves it by treating history as a replay simulator.

A recent paper by UMD and Google Deepmind researchers, Dream-RSI, tackles a huge bottleneck in recursive self-improvement: exploration strategy optimization. Usually, evaluating a new exploration policy takes many expensive online rollout cycles. You propose a new strategy, and you have to run the…

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  1. 2026-09-19 11:44 · r/reinforcementlearning
    Meta-exploration in long-horizon agent discovery is too expensive. Dream-RSI solves it by treating history as a replay simulator.

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