Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning
This addresses the problem of flexible and scalable reasoning over dynamic knowledge graphs for AI researchers, though it is incremental as it builds on existing reinforcement learning and curriculum learning techniques.
The paper tackles the challenge of Temporal Knowledge Graph Question Answering (TKGQA) by proposing Temp-R1, an autonomous agent trained with reinforcement learning, which achieves state-of-the-art performance with a 19.8% improvement over baselines on complex questions.
Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex temporal constraints. Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. We propose Temp-R1, the first autonomous end-to-end agent for TKGQA trained through reinforcement learning. To address cognitive overload in single-action reasoning, we expand the action space with specialized internal actions alongside external action. To prevent shortcut learning on simple questions, we introduce reverse curriculum learning that trains on difficult questions first, forcing the development of sophisticated reasoning before transferring to easier cases. Our 8B-parameter Temp-R1 achieves state-of-the-art performance on MultiTQ and TimelineKGQA, improving 19.8% over strong baselines on complex questions. Our work establishes a new paradigm for autonomous temporal reasoning agents. Our code will be publicly available soon at https://github.com/zjukg/Temp-R1.