Evolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents
📰 ArXiv cs.AI
Learn how Evolving-RL optimizes experience-driven self-evolving capability in agents for end-to-end adaptation to novel tasks
Action Steps
- Implement Evolving-RL to optimize experience-driven self-evolving capability within agents
- Use end-to-end optimization to adapt agents to novel tasks at deployment time
- Distill reusable experience from past interactions to improve abstraction and generalization capacities
- Apply in-context learning to enable agents to learn from few examples
- Evaluate the performance of Evolving-RL in various environments and tasks
Who Needs to Know This
Researchers and engineers working on reinforcement learning and agent development can benefit from this study to improve their models' adaptability and performance
Key Insight
💡 Evolving-RL enables agents to adapt to novel tasks by distilling reusable experience from past interactions
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🤖 Evolving-RL: End-to-end optimization for experience-driven self-evolving agents! 🚀
Key Takeaways
Learn how Evolving-RL optimizes experience-driven self-evolving capability in agents for end-to-end adaptation to novel tasks
Full Article
Title: Evolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents
Abstract:
arXiv:2605.10663v1 Announce Type: new Abstract: Experience-driven self-evolving agents aim to overcome the static nature of large language models by distilling reusable experience from past interactions, thus enabling adaptation to novel tasks at deployment time. This process places substantial demands on the foundation model's capacities for abstraction, generalization, and in-context learning. However, most existing studies focus primarily on system-level design choices, such as how experience
Abstract:
arXiv:2605.10663v1 Announce Type: new Abstract: Experience-driven self-evolving agents aim to overcome the static nature of large language models by distilling reusable experience from past interactions, thus enabling adaptation to novel tasks at deployment time. This process places substantial demands on the foundation model's capacities for abstraction, generalization, and in-context learning. However, most existing studies focus primarily on system-level design choices, such as how experience
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