@apurvasgandhi: Sub-agents are a promising inference-time scaling primitive: • Expand an agent's working memory • Divide-and-conquer ha…

X AI KOLs Timeline Papers
sub-agents inference-time-scaling reinforcement-learning agent-systems recursive-agents llm-agents model-training

Summary

RAO (Recursive Agent Optimization) is an end-to-end reinforcement learning approach for training LLM agents to spawn, delegate to, and coordinate with recursive copies of themselves, turning recursive inference into a learned capability.

Sub-agents are a promising inference-time scaling primitive: • Expand an agent's working memory • Divide-and-conquer hard problems • Solve problems faster with parallel execution But how do we train a model to best take advantage of sub-agents and make sure we get these benefits? Very excited to release RAO: Recursive Agent Optimization. RAO is an end-to-end reinforcement learning approach for training LLM agents to spawn, delegate to, and coordinate with recursive copies of themselves (that can themselves spawn other agents) - turning recursive inference into a learned capability. 1/10
Original Article

Similar Articles

Prime Agent: A Self-Improving RLM Harness

Hugging Face Daily Papers

Prime Agent is an open-source harness that uses recursive subagents and persistent computation to extend language models' long-horizon capabilities across coding and reasoning tasks, significantly improving performance on benchmarks like ARC-AGI-3.

Prime Agent: A self-improving RLM agent

Hacker News Top

Prime Intellect launches Prime Agent, a fully open-source self-improving coding harness built around Recursive Language Model (RLM) and Continual Harness abstractions, enabling persistent sub-agents and dynamic tooling via a REPL-based interface.

Recursive Multi-Agent Systems

Papers with Code Trending

This paper introduces RecursiveMAS, a framework that extends recursive scaling principles to multi-agent systems for improved collaborative reasoning efficiency and accuracy. It demonstrates significant speedups and token reduction across various benchmarks compared to standard baselines.