ProActor introduces an RL framework for proactive task scheduling that uses automated time-window annotation, proactiveness metrics, GRPO optimization, and composite rewards to improve timing while preserving action alignment.
To derive this, we employ a heuristic approach that generates automa- tion samples from system interactions and evaluates parameter presence across all cases
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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
ProActor introduces an RL framework for proactive task scheduling that uses automated time-window annotation, proactiveness metrics, GRPO optimization, and composite rewards to improve timing while preserving action alignment.