arXiv:2606.10917v1 Announce Type: new Abstract: Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static training environments, which hinder broader generalization. To address these limitations, this paper introduces Role-Agent, \textcolor{black}{a framework} that harnesses a single LLM to function concurrently as both the agent and the environment, enabling a bootstrapped co-evolution. Role-Agent comprises two synergistic components: World-In-Agent (WIA) and Agent-In-World (
The rapid advancement of large language models (LLMs) has led to increased focus on autonomous agents, making innovations in their training and generalization particularly timely.
This development offers a novel approach to training LLM agents by enabling self-bootstrapping, potentially accelerating the development of more capable and adaptive AI systems without extensive external data.
The method of training LLM agents shifts from static environments and external feedback to a dynamic, self-contained co-evolutionary process, reducing dependency on human-curated datasets.
- · AI developers
- · LLM research labs
- · Software automation sector
- · Traditional data annotation services
- · Companies relying on static AI models
More robust and generalizable AI agents are developed, enhancing their performance across complex tasks.
Reduced need for human-in-the-loop training and data collection, significantly lowering development costs and accelerating deployment.
The emergence of fully autonomous, self-improving AI systems capable of continuous learning and adaptation in real-world scenarios.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI