Evolving-RL: Joint Optimization of Experience-Driven Self-Evolving Agents
Researchers propose Evolving-RL, a framework that jointly optimizes experience extraction and utilization in large language model-based agents. Existing self-evolving systems focus on system-level design, neglecting model capabilities. Evolving-RL treats self-evolution as a unified process, using reinforcement learning to improve both abstraction and in-context learning. The approach centers on end-to-end optimization of the agent's ability to adapt from past interactions. The paper is available on arXiv under ID 2605.10663.
Key facts
- Evolving-RL is an algorithmic framework for self-evolving agents
- It jointly optimizes experience extraction and utilization
- Existing studies focus on system-level design, not model capabilities
- The framework uses reinforcement learning for optimization
- It aims to improve abstraction, generalization, and in-context learning
- The paper is published on arXiv with ID 2605.10663
- Self-evolving agents overcome static nature of LLMs by distilling experience
- The approach treats self-evolution as a unified process
Entities
Institutions
- arXiv