Update README.md

README.md

@@ -12,16 +12,16 @@ datasets:
 - internlm/CapRL-2M
 ---
 
-# CapRL-Qwen3VL-2B 
+# CapRL
 📖<a href="https://arxiv.org/abs/2509.22647">Paper</a> | 🏠<a href="https://github.com/InternLM/CapRL">Github</a> | 🤗<a href="https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189">CapRL Collection</a> | 🤗<a href="https://huggingface.co/papers/2509.22647">Daily Paper</a> 
 
-### CapRL2.0 Series
-🤗<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-2B">CapRL-Qwen3VL-2B Model</a> | 🤗<a href="https://huggingface.co/internlm/CapRL-Qwen3VL-4B">CapRL-Qwen3VL-4B Model</a>
-
-### CapRL1.0 Series
-🤗<a href="https://huggingface.co/internlm/CapRL-3B">CapRL-Qwen2.5VL-3B Model</a> |🤗<a href="https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B">CapRL-InternVL3.5-8B Model</a> |
-  🤗<a href="https://huggingface.co/datasets/internlm/CapRL-2M">CapRL-2M Dataset</a> ｜🤗<a href="https://huggingface.co/mradermacher/CapRL-3B-GGUF">CapRL-3B-GGUF</a> ｜🤗<a href="https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF">CapRL-3B-i1-GGUF</a>
+### CapRL Series Model & Dataset
+| Series | Models & Resources |
+| :--- | :--- |
+| **CapRL 2.0 Series** | [🤗 CapRL-Qwen3VL-2B](https://huggingface.co/internlm/CapRL-Qwen3VL-2B) \| [🤗 CapRL-Qwen3VL-4B](https://huggingface.co/internlm/CapRL-Qwen3VL-4B) |
+| **CapRL 1.0 Series** | [🤗 CapRL-Qwen2.5VL-3B](https://huggingface.co/internlm/CapRL-3B) \| [🤗 CapRL-InternVL3.5-8B](https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B) \| [📊 CapRL-2M Dataset](https://huggingface.co/datasets/internlm/CapRL-2M) \| [📦 CapRL-3B-GGUF](https://huggingface.co/mradermacher/CapRL-3B-GGUF) \| [📦 CapRL-3B-i1-GGUF](https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF) |
 
+### CapRL-Qwen3VL-2B
 We are excited to release the **CapRL 2.0 series**: **CapRL-Qwen3VL-2B** and **CapRL-Qwen3VL-4B**. These models feature fewer parameters while delivering even more powerful captioning performance. 
 Notably, **CapRL-Qwen3VL-2B outperforms both CapRL-Qwen2.5VL-3B and Qwen2.5VL-72B in captioning tasks**.
 This leap in efficiency is driven by our upgraded training recipe, which includes a more rigorous QA data filter and a significantly more diverse image dataset. We welcome everyone to try them out!