Noam Brown
Research scientist at OpenAI
Researches reasoning, reinforcement learning and multi-agent systems, with earlier coauthored work on poker search and language-guided negotiation.
Why read Noam Brown?
Read Brown to understand why an AI system might benefit from searching or spending more time on a problem. His ReBeL paper, coauthored with three colleagues, combines learning and search in games with hidden information. Its guarantees depend on a specific two-player setting. That gives you a concrete example of what a search method establishes before applying the intuition to language models.
His team's CICERO paper shows another connection: a language model generates messages conditioned on a strategy, rather than choosing every action through free-form text alone. In his September 2026 interview, Brown also explains why large agent-count demonstrations need smaller comparison runs to isolate their benefit. Start with those answers, then the CICERO architecture to distinguish an impressive outcome from evidence about which component caused it.
Selected work
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Noam Brown on parallel agents and reasoning
Original interview with a written transcript. In the opening discussion, Brown explains the need for smaller agent-count comparisons. No mathematical background is needed for that passage.
Cite this selection
XFollowList, “Noam Brown on parallel agents and reasoning” reading notes in the Noam Brown profile. https://xfollowlist.com/people/noam-brown#work-5916add02dc8
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original work published .
Profile-source checking date and references · Dated reading map
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Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Team research, with Brown as a corresponding author. Begin with the CICERO overview and Figure 1: the planner supplies intents, and the language model turns them into negotiable messages.
Cite this selection
XFollowList, “Human-level play in the game of Diplomacy by combining language models with strategic reasoning” reading notes in the Noam Brown profile. https://xfollowlist.com/people/noam-brown#work-48ac29eb0feb
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original work published .
Profile-source checking date and references · Dated reading map
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Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Coauthored research introducing ReBeL. The opening game example explains why hidden information changes search. Section 9 confines the guarantees to two-player zero-sum games, rather than general language-model reasoning.
Cite this selection
XFollowList, “Combining Deep Reinforcement Learning and Search for Imperfect-Information Games” reading notes in the Noam Brown profile. https://xfollowlist.com/people/noam-brown#work-9ec3e64624e9
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original work published .
Profile-source checking date and references · Dated reading map
Projects & roles
Sources & further reading
Profile sources checked . This describes checking these references, separately from the publication dates of selected works.
- His current biography identifies OpenAI, describes his research and links @polynoamial.Link to this source note
- Current X metadata names Noam Brown, identifies OpenAI and links noambrown.com.Link to this source note
- Read for the hidden-information example, method and restricted theoretical guarantees.Link to this source note
- The dated interview transcript separates Brown's answers from the host's framing.Link to this source note
Cite this profile’s source check
XFollowList, Noam Brown profile sources. Sources checked 2026-10-09. https://xfollowlist.com/people/noam-brown#sources-checked