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.
The agent then op- erates in a turn-by-turn manner, generating predictions while collecting performance statistics
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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.