Where RL Will Take Search — Maximilian-David Rumpf, SID.ai
Maximilian David Rumpf from SID.ai explores how search can be improved using reinforcement learning (RL), a technique where AI learns through trial and reward.
Today, agents spend 30–50 percent of their tokens on search before doing the actual work. Classical search engines cannot improve much because all decisions are locked in at design time — the engine can often see that results do not answer the question but cannot act on it. Rumpf shows that specialized models trained with RL complete the same task twenty times faster and at one hundredth the cost compared to large general models. Search suits RL well because the reward is completely clear: either you find the right document or you do not.
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