Where RL Will Take Search — Maximilian-David Rumpf, SID.ai
Delegating retrieval to a trained search subagent can keep noisy results out of the main agent’s context while cutting the token, latency, and cost burden of search-heavy workflows.
SID frames retrieval as a major agent expense: **30–50% of tokens** may be spent searching before the main task begins. Its RL-trained search model iterates against a database, adjusts filters, and returns ranked results.
For retrieval-heavy agents, isolate search behind a specialist subagent and pass only selected evidence into the main context. SID reports an average drop from roughly two minutes to **about 5 seconds**, alongside **100× lower cost** than frontier-model search.
SID frames retrieval as a major agent expense: **30–50% of tokens** may be spent searching before the main task begins. Its RL-trained search model iterates against a database, adjusts filters, and returns ranked results. For retrieval-heavy agents, isolate search behind a specialist subagent and pass only selected evidence into the main context. SID reports an average drop from roughly two minutes to **about 5 seconds**, alongside **100× lower cost** than frontier-model search. These are company-reported results across a mixed benchmark suite, with limited task-level detail in the talk. The model still does not match vector-and-reranker latency, and the claim that RL scaling has not reached a ceiling remains unproven.
This makes bounded retrieval delegation an economic and context-management choice, not only an orchestration pattern. It gives quantitative but company-reported support for moving iterative search out of the lead agent, while narrowing the prescription: the learned search layer remains workload-dependent and does not displace faster vector-and-reranker systems by default.