  Reasoning about time is essential for Large Language Models (LLMs) to
understand the world. Previous works focus on solving specific tasks, primarily
on time-sensitive question answering. While these methods have proven
effective, they cannot generalize to a wider spectrum of temporal reasoning
tasks. Therefore, we propose a crucial question: Can we build a universal
framework to handle a variety of temporal reasoning tasks? To that end, we
systematically study 38 temporal reasoning tasks. Based on the observation that
19 tasks are directly related to mathematics, we first leverage the available
mathematical dataset to set a solid foundation for temporal reasoning. However,
the in-depth study indicates that focusing solely on mathematical enhancement
falls short of addressing pure temporal reasoning tasks. To mitigate this
limitation, we propose a simple but effective self-critic temporal optimization
method to enhance the model's temporal reasoning capabilities without
sacrificing general task abilities. Finally, we develop Timo, a model designed
to excel in temporal reasoning at the 7B and 13B scales. Notably, Timo
outperforms the counterpart LLMs by 10.0 and 7.6 in average accuracy scores and
achieves the new state-of-the-art (SOTA) performance of comparable size.
Extensive experiments further validate our framework's effectiveness and its
generalization across diverse temporal tasks. The code is available at
https://github.com/zhaochen0110/Timo.
