Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents
arXiv:2606.04815v1 Announce Type: new Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learning agents for long-horizon tasks typically depend on discrete skill or past experiences retrieval with static parameters during inference, which prevents them from continuously internalizing test-time feedback like human learners. To bridge this gap, we propose Skill-enhanced Test-Time Co-Evolution (\texttt{LifeSkill}), a two-stage reinforcement learning framework for Online Lifelong Learning Agents
The increasing deployment of LLM agents in dynamic, interactive environments necessitates continuous adaptation beyond static inference, addressing a core limitation in current AI capabilities.
This research outlines a pathway for AI agents to continuously learn and adapt from real-time experience, moving beyond pre-trained static parameters and enhancing their autonomy and effectiveness.
AI agents could become significantly more capable of independent, real-time learning and task execution, reducing the need for constant human oversight and retraining for new situations.
- · AI developers
- · Robotics
- · Generative AI applications
- · Automation sector
- · Legacy AI systems
- · Fixed-model AI services
AI agents will exhibit improved performance and robustness in complex and changing environments.
The cost and time required for deploying and maintaining AI systems will decrease as agents become more self-sufficient.
This could accelerate the deployment of autonomous AI across various industries, leading to deeper automation and new types of AI-driven services.
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Read at arXiv cs.LG