Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization

2026-02-13 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

The BAO framework introduces an agentic reinforcement learning (RL) approach to train proactive large language model (LLM) agents, addressing the critical trade-off between task performance and user engagement in multi-turn interactions. Proactive agents actively plan, query, and interact with environments or users, moving beyond passive instruction following. Existing agentic RL pipelines struggle to balance task completion with user satisfaction, as excessive human feedback can reduce engagement. BAO combines behavior enhancement to improve proactive reasoning and information gathering with behavior regularization to suppress inefficient interactions, aligning agent behavior with user expectations. Evaluated on the UserRL benchmark, BAO significantly outperforms proactive agentic RL baselines and achieves comparable or superior performance to commercial LLM agents in complex multi-turn scenarios.

Key takeaway

For research scientists developing interactive LLM agents, you should consider BAO's multi-objective optimization approach to balance task performance with user engagement. This framework allows you to train agents that are both effective at task completion and mindful of user satisfaction, potentially leading to more robust and user-friendly applications. Focus on integrating behavior enhancement for reasoning and behavior regularization to manage interaction frequency.

Key insights

BAO optimizes proactive LLM agents for both task performance and user engagement using behavioral reinforcement learning.

Principles

• Balance task performance with user engagement.
• Enhance proactive reasoning and information gathering.
• Regularize inefficient or redundant interactions.

Method

BAO formulates proactive agent training as a multi-objective optimization problem within a Contextual MDP, using behavior enhancement for reasoning and behavior regularization to manage user interaction and token budget.

In practice

• Apply BAO to multi-turn interactive coding.
• Use BAO for web-automation tasks.
• Implement BAO in personalized conversational agents.

Topics

• Proactive LLM Agents
• Agentic Reinforcement Learning
• Multi-Objective Optimization
• User Engagement
• Behavioral Agentic Optimization

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.