Update README.md

README.md

@@ -21,12 +21,25 @@ pipeline_tag: text-classification
 
 ## 🔥 Highlights
 
-**Skywork-Reward-V2** is a series of reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared to the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements:
+**Skywork-Reward-V2** is a series of eight reward models designed for versatility across a wide range of tasks, trained on a mixture of 26 million carefully curated preference pairs. While the Skywork-Reward-V2 series remains based on the Bradley-Terry model, we push the boundaries of training data scale and quality to achieve superior performance. Compared to the first generation of Skywork-Reward, the Skywork-Reward-V2 series offers the following major improvements:
 
 - **Trained on a significantly larger and higher-quality preference data mixture**, consisting of **26 million preference pairs** curated via a large-scale human-LLM synergistic pipeline.
 - **State-of-the-art performance on seven major reward model benchmarks**, including RewardBench v1, RewardBench v2, PPE Preference, PPE Correctness, RMB, RM-Bench, and JudgeBench.
 - **Available in eight models across multiple sizes**, with the smallest 0.6B variant, *Skywork-Reward-V2-Qwen3-0.6B*, nearly matching the average performance of our previous best model, Skywork-Reward-Gemma-2-27B-v0.2. The largest 8B version, *Skywork-Reward-V2-Llama-3.1-8B*, surpasses all existing reward models across all benchmarks on average. Our top experimental model, *Skywork-Reward-V2-Llama-3.1-8B-40M*, **outperforms all existing reward models on every benchmark**.
 
+| Model                              | Basa Model                                                                                  |                                         Link                                         |
+|:-----------------------------------|:--------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------:|
+| Skywork-Reward-V2-Llama-3.1-8B     | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |   [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Llama-3.1-8B)   |
+| Skywork-Reward-V2-Llama-3.1-8B-40M | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Llama-3.1-8B-40M) |
+| Skywork-Reward-V2-Llama-3.2-1B     | [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |   [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Llama-3.2-1B)   |
+| Skywork-Reward-V2-Llama-3.2-3B     | [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |   [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Llama-3.2-3B)   |
+| Skywork-Reward-V2-Qwen3-0.6B       | [Qwen/Qwen3-0.6B-Instruct](https://huggingface.co/Qwen/Qwen3-0.6B-Instruct)                 |    [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-0.6B)    |
+| Skywork-Reward-V2-Qwen3-1.7B       | [Qwen/Qwen3-1.7B-Instruct](https://huggingface.co/Qwen/Qwen3-1.7B-Instruct)                 |    [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-1.7B)    |
+| Skywork-Reward-V2-Qwen3-4B         | [Qwen/Qwen3-4B-Instruct](https://huggingface.co/Qwen/Qwen3-4B-Instruct)                     |     [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-4B)     |
+| Skywork-Reward-V2-Qwen3-8B         | [Qwen/Qwen3-8B-Instruct](https://huggingface.co/Qwen/Qwen3-8B-Instruct)                     |     [🤗 Hugging Face](https://huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-8B)     |
+
+For the complete collection of models, please refer to the [Skywork-Reward-V2](https://huggingface.co/collections/Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84) collection.
+
 ## 📊 Evaluation
 
 In the following table, we categorize the models into two types: Bradley-Terry (BT) reward models and Generative reward models. The Skywork-Reward-V2 series outperforms models in both categories with much smaller model sizes.