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This paper proposes a new method for measuring whether LLM agents have internalized skills from public skill hubs, arguing that gain-based retirement rules fail to distinguish internalized skills from uncovered ones. It introduces a probe-based unaided scoring approach with cross-model validation and releases 1,251 skills with task data across four models.
R^2Orch introduces a recursive self-play paradigm for training agent orchestrators with learnable task selection and a Skill Withdrawal outer loop, eliminating hand-designed curricula and outperforming baselines across twelve reasoning and interactive datasets.
R²Orch 提出递归自博弈编排范式，通过内层自博弈与外层技能撤除循环，实现智能体编排的递归自我改进，在12个数据集上超越基线。
The tweet highlights a paper by the Meituan team on Skill0, an RL recipe for skill internalization, and references a related paper on self-distilled agentic RL.