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About this paper
Ran Yan, Wei Fu, Jiale Li, Shusheng Xu, Zhiyu Mei, Jiaxuan Gao, Jiarui Zhang, Wentai Zhang and 16 more
The paper proposes a governed loop for turning deployed enterprise agent interactions into learning data and routing them toward appropriate updates. AReaL2.0 connects an existing agent’s LLM inference traffic to trajectory collection and online RL training through data proxies and a control architecture. Its prototype demonstrates policy-model weight-update integration, not improved performance or full enterprise self-evolution.
Key insights
- Enabling enterprise-level self-evolving agents requires a fundamental shift towards co-designing agentic online RL systems, rather than solely focusing on models or algorithms.
- A standardized, credit-assignable trajectory data protocol is crucial for transforming heterogeneous agent interactions into a governed learning substrate.
- Agent evolution is multi-faceted, encompassing not only model weight updates but also modifications to memory, skills, harnesses, and tools, orchestrated by an intelligent control plane.
Summary by alphaXiv.
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