In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, including hand-designed curricula, adv
R²Orch uses recursive self-play with learnable task selection and skill withdrawal to train orchestrators without hand-designed curricula, improving performance across reasoning and interactive tasks.
In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, including hand-designed curricula, advantage collapse when a task's rollouts all reach the same outcome, and unreliable evaluation signals that judge each skill by one noisy score. To address these challenges, we propose R^2Orch, a new paradigm of Recursive Self-Play Orchestration: an inner loop of self-play that improves the orchestrator on self-chosen tasks, and an outer loop that improves how the inner loop improves. To support this paradigm, we introduce Recursive Learnability, a training objective that rewards a Proposer for…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 13 checks on 2 different days. Its best position was #1 of 30, reached on Nov 2, 15:28 UTC. It dropped off the list after Nov 3, 05:28 UTC.
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Not at the moment. It was last on the list on Nov 3, 05:28 UTC, after 2 days of trending.
#1 on Hugging Face · Daily papers, on Nov 2, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (13 checks) since.