RSIAgent Study: Broad-Then-Deep Exploration Lifts Agent Success From 56.5% to 74.5%

rohanpaul_ai · x · 2026-09-25

AI agents often fumble in unfamiliar apps, and the usual fix — collecting new data and retraining — is expensive. RSIAgent offers a training-free alternative: a curriculum agent invents practice tasks, an actor solves them with code, and a separate verifier validates each result, distilling reusable action-condition-consequence causal knowledge into a frozen memory that transfers to downstream tasks without any parameter updates.

The headline finding is about exploration strategy: across 4 tasks, agents that explored broadly across related tasks before going deep on hard cases averaged 74.54%, versus 56.50% for those attacking hard cases directly.

Practical takeaway: before deploying an agent in a new app, let it practice broad, then deep, with an independent verifier deciding what gets saved.

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RSIAgent Study: Broad-Then-Deep Exploration Lifts Agent Success From 56.5% to 74.5%

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