TTSE: A Two-Track Online Self-Evolution Framework
As Large Language Model (LLM) agents are applied in continuously interactive environments, driving the evolution of their own capabilities becomes a core problem for achieving long-term autonomy. Currently, environmental knowledge is typically treated as an external fixed input rather than as part of the agent's ongoing evolution. Reinforcement learning methods usually optimize policies through environmental interaction but tend to adapt only to fixed task distributions or single environments. This paper proposes TTSE (Two-Track Self-Evolution), a dual-track online self-evolution framework that separates evolving knowledge into FACT (environmental facts, whose reliability is continuously ...
| Authors | Ruimin Pei, Yongkang Wu, Shangyi Zheng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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