Update README.md

README.md

@@ -14,10 +14,14 @@ base_model:
 
 <p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a></p>
 
-## Introduction
-
-We present a compact yet powerful reasoning model **Ring-mini-2.0**. It has 16B total parameters, with 1.4B parameters are activated per input token (non-embedding 789M). Although **Ring-mini-2.0** is quite compact, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models, through pre-training on 20T tokens of high-quality data and enhanced through long-cot supervised fine-tuning and multi-stage reinforcement learning.
+Today, we officially release Ring-mini-2.0 — a high-performance inference-oriented MoE model deeply optimized based on the Ling 2.0 architecture. With only 16B total parameters and 1.4B activated parameters, it achieves comprehensive reasoning capabilities comparable to dense models below the 10B scale. It excels particularly in logical reasoning, code generation, and mathematical tasks, while supporting 128K long-context processing and 300+ tokens/s high-speed generation.
+Enhanced Reasoning: Joint Training with SFT + RLVR + RLHF
+Built upon Ling-mini-2.0-base, Ring-mini-2.0 undergoes further training with Long-CoT SFT, more stable and continuous RLVR, and RLHF joint optimization, significantly improving the stability and generalization of complex reasoning. On multiple challenging benchmarks (LiveCodeBench, AIME 2025, GPQA, ARC-AGI-v1, etc.), it outperforms dense models below 10B and even rivals larger MoE models (e.g., gpt-oss-20B-medium) with comparable output lengths, particularly excelling in logical reasoning.
+For a comprehensive evaluation of the quality of our reasoning models, we implemented automatic benchmarks to assess their performance including math, code and science. The results indicate **Ring-mini-2.0** achieves comparable performace with **Ring-lite-2507** while  activating only half parameters.
 
+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/OQWDT7e6BVwAAAAATGAAAAgADod9AQFr/original" width="1000"/>
+<p>
 
 ## Model Downloads
 
@@ -26,18 +30,8 @@ We present a compact yet powerful reasoning model **Ring-mini-2.0**. It has 16B
 |     **Model**      | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
 | :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
 | Ring-mini-2.0 |       16.8B       |         1.4B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-mini-2.0) |
-| Ring-lite-2507 |       16.8B       |         2.75B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite-2507) |
-
 </div>
 
-## Evaluation
-For a comprehensive evaluation of the quality of our reasoning models, we implemented automatic benchmarks to assess their performance including math, code and science. The results indicate **Ring-mini-2.0** achieves comparable performace with **Ring-lite-2507** while  activating only half parameters.
-
-<p align="center">
-    <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*mJKfTaP18DwAAAAAUIAAAAgAemJ7AQ/fmt.avif" width="1000"/>
-<p>
-
-
 ## Quickstart
 
 ### 🤗 Hugging Face Transformers