Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+datasets:
+- ulab-ai/Time-Bench
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+tags:
+- temporal-reasoning
+- reinforcement-learning
+- large-language-models
+paperswithcode:
+  arxiv_id: 2505.13508 
+model_index:
+- name: Time-R1-S1P1
+---
+<center>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/65d188a4aa309d842e438ef1/d6YiWBndm7WzANfl3e1qi.png" alt="Output Examples" width="600">
+</center>
+
+<div align="center">
+<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://arxiv.org/abs/2505.13508">📖 <strong>Paper</strong></a>
+</div>
+
+# 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 θ₁, 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 θ₁', 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 θ₂, 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" # 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}
+}