AI BriefingHow to Use RLM in Deep Agents
Key point
Deep Agents introduced RLM and dynamic subagents to improve performance in handling large-scale context.
Details
Recursive Language Models (RLM) solve the Context Rot problem by having the agent write code to dispatch subagents per context chunk, instead of stuffing all the data into a single context window.
Deep Agents introduced dynamic subagents and a lightweight code interpreter to implement this. Through this, the agent can programmatically perform the following tasks on large-scale input data.
- grep: search for specific patterns
- map: transform and map data
- reduce: aggregate data
In the OOLONG benchmark, a long-context reasoning task, Deep Agents demonstrated stable performance even at the point where existing turn-by-turn agent approaches hit their limits.
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