让AI自己当裁判，自己练自己
大模型智能体通常需要人类或外部环境反馈才能进步，但反馈慢、场景固定。这篇论文让同一个大模型同时扮演“玩家”和“游戏世界”：它每做一个动作，就自己预测下一步会怎样，然后拿预测和实际结果对比，差距就是“过程奖励”——相当于自己给自己出题、自己判分。更妙的是，它还会从失败案例中总结规律，专门找类似难度的任务反复练。在多个测试中，这种方法让模型平均提升4%以上。它不是你明天能用上的，但揭示了一个方向：AI的自我进化可能不需要外部老师，自己就能当教练。
📄 原文摘要(英文)
Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization. To address these limitations, this paper introduces Role-Agent, black{a framework} that harnesses a single LLM to function concurrently as both the agent and the environment, enabling a bootstrapped co-evolution. Role-Agent comprises two synergistic components: World-In-Agent (WIA) and Agent-In-World (AIW). In WIA, the LLM acts as the agent and predicts future states after each action; the alignment between predicted and actual states is then used as a process reward, encouraging environment-aware reasoning. In AIW, the LLM analyzes failure modes from failed trajectories and retrieves tasks with similar failure patterns, thereby reshaping the training data distribution for targeted practice. Experiments on multiple benchmarks show that Role-Agent consistently improves performance, yielding an average gain of over 4\% over strong baselines.