R²Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play
In brief
- R²Orch uses an inner self-play loop and an outer loop to optimize the inner learning process.
- Skills are withdrawn and internalized based on controlled self-play evaluations to ensure reliability.
- Outperforms baselines on 12 datasets covering QA, math, coding, and interactive tasks.
R²Orch introduces a dual-loop system where agents improve through self-play and skill internalization, addressing challenges like manual curricula and evaluation noise. Tests across twelve datasets show consistent gains over existing methods in QA, math, coding, and interactive tasks.
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