Improve model card: add metadata, links, and usage info

README.md

@@ -1,25 +1,42 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-1.5B-Instruct
+language:
+- en
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
 # DECS NRP Detector 
 
-This repository contains the NRP detector model used in the DECS algorithm. It is designed to determine
-whether a given reasoning chunk contains the ground truth signal.
+This repository contains the NRP (Next Reasoning Point) detector model used in the DECS algorithm, as presented in the paper [Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling](https://huggingface.co/papers/2509.25827). 
+
+The NRP detector is designed to determine whether a given reasoning chunk contains the ground truth signal, enabling surgically precise token-level rewards to reduce "overthinking" in reasoning models.
+
+- **Project Page:** [https://pixas.github.io/decs-iclr26-site/](https://pixas.github.io/decs-iclr26-site/)
+- **Repository:** [https://github.com/pixas/DECS](https://github.com/pixas/DECS)
+- **Paper:** [arXiv:2509.25827](https://huggingface.co/papers/2509.25827)
+
+## Usage
+
+According to the official repository, you can deploy the NRP detector using `vLLM`:
+
+```bash
+vllm serve --model pixas/DECS_NRP_DETECTOR --port 10041 
+```
 
 ## Citation
 
-If you use this model, please cite:
+If you use this model, please cite the following work:
 
 ```bibtex
-@inproceedings{jiang2026overthinking,
-title={Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling},
-author={Shuyang Jiang and Yusheng Liao and Ya Zhang and Yanfeng Wang and Yu Wang},
-booktitle={The Fourteenth International Conference on Learning Representations},
-year={2026},
-url={https://openreview.net/forum?id=kdeiRledV6}
+@inproceedings{jiang2026decs,
+  title     = {Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling},
+  author    = {Jiang, Shuyang and Tao, Xiaofeng and Zhang, Kui and Xiao, Yanghua},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  year      = {2026},
+  note      = {Oral},
+  url       = {https://arxiv.org/abs/2509.25827}
 }
 ```