RSIAgent：通过广泛探索与验证提升AI Agent在陌生应用中的表现
Agents that explored widely before tackling hard cases averaged 74.54% on 4 task…
Agent泛化是核心痛点，这篇论文提出的“先广后深+独立验证”的课程学习框架给出了可复现的工程思路，对做Agent落地的同学很有参考价值。
Agents that explored widely before tackling hard cases averaged 74.54% on 4 tasks, versus 56.50% when they went straight to the hard cases, so start wide.
在解决难题之前广泛探索的智能体，在4项任务上的平均准确率为74.54%，而直接处理难题时仅为56.50%，因此应先广泛探索。
AI agents often fumble in unfamiliar apps. The usual fix is collecting new data and retraining, which is expensive.
AI智能体经常在不熟悉的应用中操作失误。通常的解决方法是收集新数据并重新训练，但这成本高昂。
In RSIAgent, a curriculum agent invents practice tasks, an actor solves them with code, and a separate verifier checks each result. Practice starts broad across related tasks, then goes deep on the real task and its hard cases.
在RSIAgent中，课程智能体生成练习任务，执行者通过代码解决这些任务，独立的验证器检查每个结果。练习先从相关任务的广度开始，然后深入实际任务及其难题。
Overall the paper says, Before putting an agent to work in a new app, let it practice broad, then deep, with a separate verifier deciding what's saved.
总体而言，论文指出，在将智能体投入新应用工作之前，让其先进行广泛练习，再进行深入练习，并由独立验证器决定保留哪些内容。
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