# 第七章 学习、评估与进化

如果智能体每天犯同样的错误，那它不是真正的智能。本章探讨智能体的动态进化能力。不同于LLM预训练的一次性，智能体的学习应该是终身的、持续的。通过强化学习(RLHF/RLAIF) → 多维评估体系 → 持续进化机制(经验回放、自我修正、数据飞轮)的完整路径，实现行动→评估→反思→学习的闭环。

## 学习目标

完成本章后，你将能够：

1. **应用** RLHF/RLAIF进行智能体行为微调
2. **建立** 多维度的评估体系与基准测试
3. **实现** 轨迹分析与持续学习机制
4. **优化** 推理能力与对齐安全性

***

**下一节**: [7.1 从反馈中学习：RLHF 与 RLAIF](/agentic_ai_guide/di-er-bu-fen-qun-ti-zhi-neng-yu-jin-hua/07_evolution/7.1_rl.md)


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