每激活一次，推理能力躍升：將通用推理模型擴展至萬億級開放語言基座

摘要

我們推出 Ling 2.0——一個基於「每次激活皆提升推理能力」原則構建的系列化推理導向語言基礎模型。該系列在統一混合專家模型框架下，設計參數規模可從數百億擴展至一萬億，並憑藉實證縮放定律實現高稀疏度、跨尺度一致性與高效能特性。系列包含三款非思維（指令）模型：Ling-mini-2.0、Ling-flash-2.0 與 Ling-1T，總參數規模從160億至1萬億，相比稠密模型實現最高7倍激活計算效率。Ling 2.0 整合了模型架構、預訓練、後訓練及基礎設施的協同創新：採用具備多令牌預測的高稀疏MoE架構以實現高效推理，融合推理導向數據與訓練中期思維鏈激活，實施基於強化的精調技術（DFT、Evo-CoT），並實現全尺度FP8訓練與細粒度異構流水線。在萬億參數級別，Ling-1T 建立了推理準確率與計算效率的新帕累托前沿，證明稀疏激活機制與推理目標精準對齊時，可實現可擴展的高效智能。整體而言，Ling 2.0 為推進未來推理與思維模型（包括基於同底座的Ring系列）提供了連貫、開放且高效的基礎框架。

English

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

每激活一次，推理能力躍升：將通用推理模型擴展至萬億級開放語言基座

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

摘要

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