首个通过强化学习训练的自主时间知识图谱问答代理，解决复杂时序推理难题。
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning
- 扩展动作空间，融合内外部动作应对多跳时序依赖。
- 逆向课程学习先训难题，提升复杂问题推理能力，复杂题准确率领先19.8%。
- 适合需要自主时序推理的智能系统开发者，开源可复现。
时序知识图谱问答（TKGQA）极具挑战性，需对动态事实进行多跳推理并满足复杂时间约束。现有方法依赖固定流程和昂贵闭源API，灵活性与可扩展性受限。我们提出Temp-R1，首个通过强化学习训练的端到端自主代理。为缓解单步推理的认知负担，引入专用内部动作拓展动作空间。为避免简单问题上的捷径学习，采用逆向课程学习策略，优先训练复杂问题，迫使模型发展出高级推理能力后迁移至简单任务。80亿参数的Temp-R1在MultiTQ和TimelineKGQA上达到当前最优性能，复杂问题上相比强基线提升19.8%。本工作确立了自主时序推理的新范式。代码已公开于https://github.com/zjukg/Temp-R1。
原文摘要 · Abstract (English)
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. The code is available at https://github.com/zjukg/Temp-R1.
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