Improve model card: Update pipeline tag, add library name, and abstract

README.md

@@ -1,16 +1,16 @@
 ---
-name: Traffic-R1-3B (Public 0.1)
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-3B
-pipeline_tag: reinforcement-learning
+language:
+- en
+license: apache-2.0
+pipeline_tag: robotics
 tags:
 - traffic
 - reinforce LLM
 - LLM agent
-
+name: Traffic-R1-3B (Public 0.1)
+library_name: transformers
 ---
 
 ### 🚀**Traffic-R1-3B (Public 0.1)** 🚦
@@ -22,12 +22,14 @@ This model is a checkpoint based on the research in our paper:
 **Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems** 📄
 [https://arxiv.org/abs/2508.02344](https://arxiv.org/abs/2508.02344)
 
+## Abstract
+Traffic signal control (TSC) is vital for mitigating congestion and sustaining urban mobility. In this paper, we introduce Traffic-R1, a foundation model with human-like reasoning for TSC systems. Our model is developed through self-exploration and iteration of reinforced large language models (LLMs) with expert guidance in a simulated traffic environment. Compared to traditional reinforcement learning (RL) and recent LLM-based methods, Traffic-R1 offers three significant advantages. First, Traffic-R1 delivers zero-shot generalisation, transferring unchanged to new road networks and out-of-distribution incidents by utilizing its internal traffic control policies and human-like reasoning. Second, its 3B-parameter architecture is lightweight enough for real-time inference on mobile-class chips, enabling large-scale edge deployment. Third, Traffic-R1 provides an explainable TSC process and facilitates multi-intersection communication through its self-iteration and a new synchronous communication network. Extensive benchmarks demonstrate that Traffic-R1 sets a new state of the art, outperforming strong baselines and training-intensive RL controllers. In practice, the model now manages signals for more than 55,000 drivers daily, shortening average queues by over 5% and halving operator workload.
+
 ## **Compatibility & Reproducibility** 🛠️
 
-This model supports a wide range of deployment methods compatible with the Qwen architecture. You can easily use it in a chat mode to interactively discuss traffic-related scenarios. 
+This model supports a wide range of deployment methods compatible with the Qwen architecture, including those provided by the `transformers` library. You can easily use it in a chat mode to interactively discuss traffic-related scenarios.
 For more detailed information on deployment, please refer to the official [Qwen documentation](https://qwen.readthedocs.io/en/latest/).
 
-
 The model is compatible with the signal control evaluation code provided by **LLMLight** [https://github.com/usail-hkust/LLMTSCS]. You can quickly reproduce our results with minor changes to the prompt format.
 
 A big thanks to these excellent projects! 👏
@@ -40,7 +42,6 @@ We plan to release our evaluation code (most necessary) and training code soon.
 
 We are working on upgrading base mode Qwen 2.5->Qwen 3 for latest features.
 
-
 ---
 
 ### **Important Notice** ⚠️