自进化智能体论文：将日常操作转化为安全学习数据

Great paper on Self-evolving agents.

Enterprise agents cannot truly improve until their messy daily work becomes safe learning data.

A future enterprise agent may improve by updating memory before changing its underlying model.

The problem is that deployed agents generate many useful traces, but teams usually improve them through slow manual inspection, prompt edits, retraining, and redeployment.

They propose a 3-part mechanism: first, record every agent step in a shared learning-ready format; second, use a data proxy to clean, govern, store, and replay real agent work; third, use a control layer to decide whether to update memory, skills, prompts, tools, or model weights.

AREAL2.0 shows one narrow version of this idea, where live agent LLM calls are routed through an online RL service so real interaction traces can train future model updates.

The authors say the main gap is a system that turns agent activity into usable learning data, not another clever optimizer.

Future agents will need safe, replayable ways to update memory, skills, prompts, tools, or models without becoming uncontrolled.

– arxiv. org/abs/2607.01120v1

Title: "Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents"