arXiv:2602.11351v2 Announce Type: replace-cross Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world, user-centric applications. Agentic reinforcement learning (RL) has recently emerged as a promising solution for training such agents in multi-turn settings, allowing them to learn long-horizon decision-making strategies. However, existing pipelines face a critical challenge in balancing task performance with user engage
The paper addresses the ongoing challenge of developing effective large language model agents that can perform complex, multi-turn tasks autonomously, crucial for real-world applications.
Sophisticated readers should care because this research directly contributes to overcoming limitations in AI agents, accelerating their capability to perform complex work and interact with users effectively across various industries.
The proposed 'Behavioral Agentic Optimization' method offers a pathway to balance task performance and user engagement in proactive AI agents, potentially leading to more robust and commercially viable AI solutions.
- · AI agent developers
- · SaaS companies integrating agents
- · End-users of AI applications
- · Companies reliant on passive AI assistants
- · Traditional workflow automation providers
Improved performance and reliability of large language model agents for complex tasks.
Accelerated adoption of AI agents across various white-collar and user-centric applications, impacting employment and industry structures.
The development of highly autonomous, self-optimizing AI systems that fundamentally alter human-computer interaction paradigms.
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.LG