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

arXiv: https://arxiv.org/abs/2510.22115 · HTML: https://mlsanity.com/paper/2510.22115/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- First posted on arXiv 25 Oct 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)
- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Built on by (most cited first, 30 of 34)

- [LLaDA2.0: Scaling Up Diffusion Language Models to 100B](https://mlsanity.com/paper/2512.15745/) (2025)
- [UI-Venus-1.5 Technical Report](https://mlsanity.com/paper/2602.09082/) (2026)
- [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://mlsanity.com/paper/2604.12374/) (2026)
- [Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem](https://mlsanity.com/paper/2512.24873/) (2025)
- [Scalable Training of Mixture-of-Experts Models with Megatron Core](https://mlsanity.com/paper/2603.07685/) (2026)
- [LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model](https://mlsanity.com/paper/2604.20796/) (2026)
- [Rethinking Language Model Scaling under Transferable Hypersphere Optimization](https://mlsanity.com/paper/2603.28743/) (2026)
- [HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2511.20520/) (2025)
- [TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration](https://mlsanity.com/paper/2602.08404/) (2026)
- [MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging](https://mlsanity.com/paper/2601.17858/) (2026)
- [TWEO: Transformers Without Extreme Outliers Enables FP8 Training And Quantization For Dummies](https://mlsanity.com/paper/2511.23225/) (2025)
- [Nanbeige4-3B Technical Report: Exploring the Frontier of Small Language Models](https://mlsanity.com/paper/2512.06266/) (2025)
- [Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale](https://mlsanity.com/paper/2606.15079/) (2026)
- [SSPO: Subsentence-level Policy Optimization](https://mlsanity.com/paper/2511.04256/) (2025)
- [Deconstructing Pre-training: Knowledge Attribution Analysis in MoE and Dense Models](https://mlsanity.com/paper/2601.08383/) (2026)
- [Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding](https://mlsanity.com/paper/2605.00342/) (2026)
- [PowLU: An Activation Function for Stable Pre-Training of LLMs](https://mlsanity.com/paper/2605.25704/) (2026)
- [Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models](https://mlsanity.com/paper/2511.23319/) (2025)
- [SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence](https://mlsanity.com/paper/2512.22334/) (2025)
- [SERE: Similarity-based Expert Re-routing for Efficient Batch Decoding in MoE Models](https://mlsanity.com/paper/2602.07616/) (2026)
- [Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models](https://mlsanity.com/paper/2604.10556/) (2026)
- [Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks](https://mlsanity.com/paper/2605.01417/) (2026)
- [UI-Venus-2 Technical Report](https://mlsanity.com/paper/2609.00028/) (2026)
- [HeartBench: Probing Core Dimensions of Anthropomorphic Intelligence in LLMs](https://mlsanity.com/paper/2512.21849/) (2025)
- [On Representation Redundancy in Large-Scale Instruction Tuning Data Selection](https://mlsanity.com/paper/2602.13773/) (2026)
- [SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling](https://mlsanity.com/paper/2605.28179/) (2026)
- [AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert](https://mlsanity.com/paper/2511.18314/) (2025)
- [OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs](https://mlsanity.com/paper/2511.19023/) (2025)
- [AR-MAP: Are Autoregressive Large Language Models Implicit Teachers for Diffusion Large Language Models?](https://mlsanity.com/paper/2602.02178/) (2026)
- [An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs](https://mlsanity.com/paper/2603.14463/) (2026)

## Abstract (arXiv)

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.