title: Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
summary: Large Language Models (LLMs) demonstrate impressive capabilities but lack
  robust temporal intelligence, struggling to integrate reasoning about the past
  with predictions and plausible generations of the future. Meanwhile, existing
  methods typically target isolated temporal skills, such as question answering
  about past events or basic forecasting, and exhibit poor generalization,
  particularly when dealing with events beyond their knowledge cutoff or
  requiring creative foresight. To address these limitations, we introduce
  Time-R1, the first framework to endow a moderate-sized (3B-parameter)
  LLM with comprehensive temporal abilities: understanding, prediction, and
  creative generation. Our approach features a novel three-stage development
  path; the first two constitute a reinforcement learning (RL)
  curriculum driven by a meticulously designed dynamic rule-based reward system.
  This framework progressively builds (1) foundational temporal understanding and
  logical event-time mappings from historical data, (2) future event prediction
  skills for events beyond its knowledge cutoff, and finally (3) enables
  remarkable generalization to creative future scenario generation without any
  fine-tuning. Strikingly, experiments demonstrate that Time-R1 outperforms
  models over 200 times larger, including the state-of-the-art 671B DeepSeek-R1,
  on highly challenging future event prediction and creative scenario generation
  benchmarks. This work provides strong evidence that thoughtfully engineered,
  progressive RL fine-tuning allows smaller, efficient models to achieve superior
  temporal performance, offering a practical and scalable path towards truly
  time-aware AI. To foster further research, we also release Time-Bench,
  a large-scale multi-task temporal reasoning dataset derived from 10 years of
  news data, and our series of Time-R1 checkpoints.
paper_authors._id: 683148c0018bba5b656c94e3
paper_authors.name: Zijia Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:12:18.318Z
paper_authors._id: 65d188a4aa309d842e438ef1
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paper_authors.fullname: Zijia Liu
paper_authors.type: user
paper_authors.user: m-serious
paper_authors._id: 683148c0018bba5b656c94e4
paper_authors.name: Peixuan Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:55:11.355Z
paper_authors._id: 638d601b5e14c2f38678fb3a
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paper_authors.fullname: 韩沛煊
paper_authors.type: user
paper_authors.user: HakHan
paper_authors._id: 683148c0018bba5b656c94e5
paper_authors.name: Haofei Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:09.595Z
paper_authors._id: 636453547cf2c0b4f0a3ee1e
paper_authors.avatarUrl: /avatars/a453340b44d08eec2281ecbe5e993707.svg
paper_authors.fullname: Haofei Yu
paper_authors.type: user
paper_authors.user: lwaekfjlk
paper_authors._id: 683148c0018bba5b656c94e6
paper_authors.name: Haoru Li
paper_authors._id: 683148c0018bba5b656c94e7
paper_authors.name: Jiaxuan You
paper_title: Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
paper_summary: Large Language Models (LLMs) demonstrate impressive capabilities but lack
  robust temporal intelligence, struggling to integrate reasoning about the past
  with predictions and plausible generations of the future. Meanwhile, existing
  methods typically target isolated temporal skills, such as question answering
  about past events or basic forecasting, and exhibit poor generalization,
  particularly when dealing with events beyond their knowledge cutoff or
  requiring creative foresight. To address these limitations, we introduce
  Time-R1, the first framework to endow a moderate-sized (3B-parameter)
  LLM with comprehensive temporal abilities: understanding, prediction, and
  creative generation. Our approach features a novel three-stage development
  path; the first two constitute a reinforcement learning (RL)
  curriculum driven by a meticulously designed dynamic rule-based reward system.
  This framework progressively builds (1) foundational temporal understanding and
  logical event-time mappings from historical data, (2) future event prediction
  skills for events beyond its knowledge cutoff, and finally (3) enables
  remarkable generalization to creative future scenario generation without any
  fine-tuning. Strikingly, experiments demonstrate that Time-R1 outperforms
  models over 200 times larger, including the state-of-the-art 671B DeepSeek-R1,
  on highly challenging future event prediction and creative scenario generation
  benchmarks. This work provides strong evidence that thoughtfully engineered,
  progressive RL fine-tuning allows smaller, efficient models to achieve superior
  temporal performance, offering a practical and scalable path towards truly
  time-aware AI. To foster further research, we also release Time-Bench,
  a large-scale multi-task temporal reasoning dataset derived from 10 years of
  news data, and our series of Time-R1 checkpoints.
paper_ai_summary: A novel framework, Time-R1, enhances moderate-sized LLMs with comprehensive temporal abilities through a reinforcement learning curriculum, outperforming larger models on future event prediction and creative scenario generation benchmarks.
paper_ai_keywords: Large Language Models reinforcement learning RL curriculum rule-based reward system temporal understanding event-time mappings future event prediction creative scenario generation Time-Bench Time-R1 checkpoints