---
title: "Where RL Will Take Search"
category: "talks"
date: "2026-06-29"
time: "2:50pm-3:10pm"
track: "Search & Retrieval"
room: "Track 3"
speakers: ["Maximilian-David Rumpf", "Lotte Seifert"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Search & Retrieval"
scheduleRoom: "Track 3"
scheduleLabels: ["Search & Retrieval", "Track 3", "session", "confirmed"]
---
# Where RL Will Take Search

## Conference Context
- Date/time: 2026-06-29 · 2:50pm-3:10pm
- Track/room: Search & Retrieval · Track 3
- Speaker(s): Maximilian-David Rumpf, Lotte Seifert
- Session type/status: session · confirmed

- Track: Search & Retrieval
- Room: Track 3
- Session type: session
- Status: confirmed

## Session Description
Search is having its Bitter Lesson moment. By turning search into an RL problem, we can finally scale search quality with compute! RL is extremely sample efficient when compared to classical search training objectives and we see no ceiling to how far we can scale this new paradigm. We cover the training of SID-1, the first RL-trained search model, and how search will look like post-RL.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[maximilian-david-rumpf]]
- [[lotte-seifert]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Where RL Will Take Search ## Conference Context - Date/time: 2026-06-29 · 2:50pm-3:10pm - Track/room: Search & Retrieval · Track 3 - Speaker(s): Maximilian-David Rumpf, Lotte Seifert - Session type/status: session · confirmed - Track: Search & Retrieval - Room: Track 3 - Session type: session - Status: confirmed ## Session Description Search is having its Bitter Lesson moment. By turning search into an RL problem, we can finally scale search quality with compute! RL is extremely sample efficient when compared to classical search training objectives and we see no ceiling to how far we can scale this new paradigm. We cover the training of SID-1, the first RL-trained search model, and how search will look like post-RL.

### Speaker And Company Context
- [[maximilian-david-rumpf|Maximilian-David Rumpf]] — CEO at [[sid-ai|SID.ai]].
- [[lotte-seifert|Lotte Seifert]] — Founder at [[sid-ai|SID AI]].

### Topics Covered
- [[agentic-search]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.