ulab-uiuc/Time-R1
Time-R1: Framework and resources for endowing LLMs with comprehensive temporal reasoning (understanding, prediction, creative generation) using a novel three-stage RL curriculum. Includes the Time-Bench dataset and pre-trained models.
This project helps large language models (LLMs) better understand, predict, and creatively generate information related to time. It takes historical text data, like news articles, as input and produces an LLM capable of robust temporal reasoning. Anyone who uses or develops applications with LLMs for tasks involving event sequencing, future prediction, or scenario planning would benefit.
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Use this if you need an LLM to accurately interpret past events, forecast future occurrences, or generate plausible timelines and scenarios.
Not ideal if your primary need is for non-temporal text generation or analysis, or if you are looking for a ready-to-use application rather than a framework for enhancing LLMs.
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Python
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Apache-2.0
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Jun 11, 2025
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