Update README.md

README.md

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 <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>&nbsp;&nbsp; | &nbsp;&nbsp;🐙 <a href="https://zenmux.ai/inclusionai/ring-1t?utm_source=hf_inclusionAI">Experience Now</a></p>
 
+# Ring-1T, flow state leads to sudden enlightenment
+
+Today, we officially launch the trillion-parameter thinking model, Ring-1T. It is open-source upon release—developers can download the model weights from Hugging Face and ModelScope, or experience direct chat interactions and API calls via the Ling Chat page and ZenMux (links provided at the end of the article).
+
+Building upon the preview version released at the end of last month, Ring-1T has undergone continued scaling with large-scale verifiable reward reinforcement learning (RLVR) training, further unlocking the natural language reasoning capabilities of the trillion-parameter foundation model. Through RLHF training, the model's general abilities have also been refined, making this release of Ring-1T more balanced in performance across various tasks.
+
+Ring-1T adopts the Ling 2.0 architecture and is trained on the Ling-1T-base foundation model, which contains 1 trillion total parameters with 50 billion activated parameters, supporting a context window of up to 128K tokens. Leveraging our self-developed icepop reinforcement learning stabilization method and the efficient reinforcement learning system ASystem (whose AReal framework is already open-source), we have achieved smooth scaling of MoE architecture reinforcement learning—from tens of billions (Ring-mini-2.0) to hundreds of billions (Ring-flash-2.0) to trillions (Ring-1T) of parameters—significantly enhancing the model's deep reasoning and natural language inference capabilities.
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+## Continuously Evolving Deep Reasoning Capabilities
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+To evaluate the deep reasoning capabilities of Ring-1T, we selected representative open-source reasoning models (Ring-1T-preview, Deepseek-V3.1-Terminus-Thinking, Qwen-235B-A22B-Thinking-2507) and closed-source APIs (Gemini-2.5-pro and GPT-5-Thinking(High)) as benchmarks. First, compared to the previously open-sourced preview version, Ring-1T demonstrates more balanced performance across various tasks. Furthermore, Ring-1T achieves open-source leading performance on challenging reasoning benchmarks such as math competitions (AIME 25, HMMT 25), code generation (LiveCodeBench, CodeForce), and logical reasoning (ARC-AGI-1). It also exhibits strong competitiveness in comprehensive tasks (Arena-Hard-v2.0), healthcare (HealthBench), and creative writing (Creative Writing v3).
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+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_d2byvp/afts/img/5TBESJNjsbAAAAAAYYAAAAgADod9AQFr/original" width="100"/>
+</p>
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+Although we have implemented string-level and semantic-level contamination filtering for benchmark tasks across all training stages—including pre-training, fine-tuning instructions, and reinforcement learning prompts—rigorous decontamination for earlier published benchmarks remains a significant challenge in the industry. To more objectively analyze Ring-1T's deep reasoning capabilities, we conducted tests using the IMO 2025 (International Mathematical Olympiad) held in July this year and the recently concluded ICPC World Finals 2025 (International Collegiate Programming Contest World Finals).
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 ## Model Downloads