ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
The paper introduces ProActor, a framework for proactive task scheduling using timing-aware reinforcement learning. It emphasizes the need for agents to autonomously anticipate user needs and optimize their actions accordingly. The study demonstrates significant improvements in proactive timing while maintaining action consistency compared to existing methods.
- ▪ProActor integrates a domain-agnostic automated annotation methodology for scalable reinforcement learning.
- ▪The framework includes systematic proactiveness metrics that capture timing quality and action alignment.
- ▪Experiments show that ProActor achieves 4-8x speedups in training while improving proactive timing.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24900 (cs) [Submitted on 24 May 2026] Title:ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Authors:Lei Ding, Bin He, Chenguang Wang, Yang Liu View a PDF of the paper titled ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents, by Lei Ding and 3 other authors View PDF HTML (experimental) Abstract:Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate moments - fundamentally shifting from reactive systems that await explicit instructions. However, existing approaches lack generalizable end-to-end solutions for measuring and optimizing such anticipatory behaviors.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
