Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
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.
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Zijia Liu
claimed_verified
2025-05-26T08:12:18.318Z
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Zijia Liu
user
m-serious
683148c0018bba5b656c94e4
Peixuan Han
claimed_verified
2025-05-30T06:55:11.355Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/638d601b5e14c2f38678fb3a/Elu-TTd97lGy7YL7eKRuZ.jpeg
韩沛煊
user
HakHan
683148c0018bba5b656c94e5
Haofei Yu
claimed_verified
2025-05-27T07:55:09.595Z
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Haofei Yu
user
lwaekfjlk
683148c0018bba5b656c94e6
Haoru Li
683148c0018bba5b656c94e7
Jiaxuan You
Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
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.
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.
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