arXiv:2606.07367v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments, yet training them to perform reliable long-horizon decision making remains a fundamental challenge. A key difficulty lies in credit assignment: agents often receive delayed rewards only at the end of episodes. In this paper, we propose Q-Evolve, a self-evolving framework for LLM agents that unifies automatic process-reward labeling and policy learning within a principled in-distribution reinforcement learning paradigm. In eac
The rapid advancement of large language models is driving research into more autonomous and reliable AI systems capable of complex decision-making in interactive environments.
Improving LLM agents' ability to learn and perform reliable long-horizon tasks addresses a key limitation in current AI applications, leading to more capable and autonomous systems.
The development of frameworks like Q-Evolve could significantly enhance the efficiency and effectiveness of training LLM agents for complex, real-world applications.
- · AI software developers
- · Automation companies
- · Researchers in reinforcement learning
- · Tasks requiring manual oversight of agent training
- · Companies relying on less efficient agent training methods
More sophisticated and self-improving AI agents become feasible across various domains.
Reduced human intervention needed for training complex AI systems, accelerating AI deployment.
Increased integration of autonomous agents into white-collar workflows, potentially displacing traditional SaaS layers and human tasks.
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