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.
• (Optional, if annotation budget per- mits) The fullvalid triggering window [t∗ a, tend a ], capturing the range of turns over which triggering remains appropri- ate
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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.