---
base_model:
- Qwen/Qwen2.5-3B-Instruct
datasets:
- ulab-ai/Time-Bench
license: apache-2.0
tags:
- temporal-reasoning
- reinforcement-learning
- large-language-models
paperswithcode:
arxiv_id: 2505.13508
library_name: transformers
pipeline_tag: text-generation
---
# Time-R1 Model Series
This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper "Time-R1: Towards Comprehensive Temporal Reasoning in LLMs". Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
These models are trained using the [Time-Bench dataset](https://huggingface.co/datasets/ulab-ai/Time-Bench).
## Model Checkpoints
We provide several checkpoints representing different stages of the Time-R1 training process:
### Stage 1: Temporal Comprehension Models
These models are trained to develop foundational temporal understanding.
* **[Time-R1-S1P1](https://huggingface.co/ulab-ai/Time-R1-S1P1):** Checkpoint after Phase 1 of Stage 1 training.
* *Focus: Foundational logic on easy timestamp inference tasks.*
* **[Time-R1-S1P2](https://huggingface.co/ulab-ai/Time-R1-S1P2):** Checkpoint after Phase 2 of Stage 1 training.
* *Focus: Full task exploration on all Stage 1 subtasks with mixed difficulty.*
* **[Time-R1-Theta1](https://huggingface.co/ulab-ai/Time-R1-Theta1):** Checkpoint ΞΈβ, after Phase 3 (full Stage 1 training).
* *Focus: Refined precision on all Stage 1 subtasks under stricter evaluation.*
* **[Time-R1-Theta1_prime](https://huggingface.co/ulab-ai/Time-R1-Theta1_prime):** Ablation model ΞΈβ', trained for Stage 1 without the dynamic reward design.
* *Focus: Serves as a baseline to evaluate the efficacy of the dynamic reward curriculum.*
### Stage 2: Future Event Time Prediction Model
This model builds upon Stage 1 capabilities to predict future event timings.
* **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint ΞΈβ, after Stage 2 training.
* *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
Please refer to the [main paper](https://arxiv.org/abs/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
## How to Use
For loading and using these models, please refer to the example scripts and documentation provided in our [GitHub repository](https://github.com/ulab-uiuc/Time-R1).
Typically, you can load the models using the Hugging Face `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Example for one of the models (replace with the specific model name)
model_name = "ulab-ai/Time-R1-Theta1_prime" # Or your specific Hugging Face model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Further usage instructions would go here or in the repository
```
## Citations
```bibtex
@article{liu2025time,
title={Time-R1: Towards Comprehensive Temporal Reasoning in LLMs},
author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
journal={arXiv preprint arXiv:2505.13508},
year={2025}
}