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⏱️ Expected Reading Time: 8 minutes

Introduction

The field of large language models has witnessed another groundbreaking advancement with the release of Ring-flash-2.0 by inclusionAI. This innovative thinking model represents a significant leap forward in Mixture of Experts (MoE) architecture, combining exceptional performance with remarkable efficiency. Built upon the foundation of Ling-flash-2.0-base, Ring-flash-2.0 introduces revolutionary training methodologies and architectural optimizations that set new standards for complex reasoning tasks.

What makes Ring-flash-2.0 particularly noteworthy is its ability to deliver performance comparable to much larger models while maintaining exceptional computational efficiency. With 100 billion total parameters but only 6.1 billion activated per inference, this model demonstrates how intelligent architecture design can achieve outstanding results without proportional resource consumption.

Model Architecture and Specifications

Core Architecture Design

Ring-flash-2.0 employs a sophisticated MoE architecture that represents the evolution of the Ling 2.0 series. The model’s design philosophy centers around maximizing performance while minimizing computational overhead through several key architectural innovations:

Parameter Configuration:

  • Total parameters: 100 billion
  • Activated parameters per inference: 6.1 billion (4.8B non-embedding)
  • Expert activation ratio: 1/32
  • Integration of MTP (Mixture of Tensor Parallelism) layers

The model’s architecture achieves a remarkable balance between capacity and efficiency. By activating only 6.1% of its total parameters during inference, Ring-flash-2.0 delivers performance comparable to dense models with approximately 40 billion parameters while maintaining significantly faster inference speeds.

Efficiency Optimizations

The structural optimizations implemented in Ring-flash-2.0 enable exceptional inference performance. When deployed on just four H20 GPUs, the model achieves generation speeds exceeding 200 tokens per second. This high-speed inference capability makes Ring-flash-2.0 particularly suitable for high-concurrency scenarios where traditional thinking models might face scalability challenges.

The low activation ratio combined with high sparsity design significantly reduces the computational burden during inference, making advanced reasoning capabilities more accessible for real-world applications. This efficiency breakthrough addresses one of the primary concerns in deploying large-scale thinking models in production environments.

The Revolutionary IcePop Algorithm

Addressing Training-Inference Gaps

One of the most significant contributions of Ring-flash-2.0 is the introduction of the IcePop algorithm, which addresses critical challenges in reinforcement learning for MoE models. Traditional RL approaches for MoE architectures face substantial difficulties due to precision discrepancies between training and inference phases, particularly as sequence lengths and training steps increase.

The core problem that IcePop solves relates to the progressive widening of gaps between training and inference precision. In conventional approaches, when the relative difference between training and inference probabilities for the same token exceeds 5%, the training process effectively fails. This limitation has historically posed significant challenges for long-horizon reinforcement learning with lengthy sequences.

Distribution Calibration Mechanism

IcePop introduces an innovative solution through distribution calibration via masked bidirectional truncation. This approach effectively narrows the gap between training and inference phases through two key mechanisms:

Bidirectional Truncation: The algorithm performs sophisticated truncation operations that address both scenarios where training probability significantly exceeds inference probability and the reverse situation where training probability falls substantially below inference probability. This bidirectional approach ensures balanced optimization across different probability distributions.

Masking Strategy: Tokens exhibiting excessively large discrepancies between training and inference phases are strategically excluded from gradient computation. This selective masking prevents unstable gradients from destabilizing the training process while maintaining learning efficiency for well-aligned tokens.

The IcePop algorithm represents a fundamental advancement in making RL training stable and effective for MoE architectures, enabling sustained improvement in complex reasoning capabilities throughout extended training cycles.

Multi-Stage Training Pipeline

Comprehensive Training Methodology

Ring-flash-2.0 employs a sophisticated multi-stage training pipeline designed to comprehensively enhance the model’s capabilities across different domains. The training process consists of three distinct phases, each targeting specific aspects of model performance:

Phase 1: Long-CoT SFT (Supervised Fine-Tuning) The initial phase focuses on equipping the base Ling-flash-2.0 model with diverse thinking patterns through lightweight Long-Chain-of-Thought supervised fine-tuning. This foundation phase establishes the model’s reasoning framework and prepares it for more advanced training stages.

Phase 2: RLVR (Reinforcement Learning with Verifiable Rewards) The second phase implements reinforcement learning with verifiable rewards to continuously stimulate and enhance the model’s reasoning potential. This stage focuses on developing robust reasoning capabilities that can be objectively evaluated and improved through reward-based optimization.

Phase 3: RLHF (Reinforcement Learning from Human Feedback) The final phase incorporates human feedback to refine the model’s general abilities and ensure alignment with human preferences and expectations. This stage balances the model’s enhanced reasoning capabilities with practical usability and safety considerations.

Training Strategy Optimization

During the development process, the team compared joint training approaches that combined RLVR and RLHF with the ultimately adopted two-staged RL pipeline. While both methodologies demonstrated similar effectiveness in experimental settings, the two-staged approach proved superior from an engineering efficiency perspective.

The differing difficulty levels between RLVR and RLHF tasks created challenges in joint training scenarios. RLHF tasks typically involve shorter model rollouts compared to RLVR tasks, leading to more long-tail generations during joint training. The two-staged approach addresses these challenges by allowing each training phase to focus on its specific objectives without interference from conflicting optimization signals.

Performance Benchmarks and Achievements

Comprehensive Evaluation Results

Ring-flash-2.0 demonstrates exceptional performance across a diverse range of challenging benchmarks, establishing new standards for thinking models in multiple domains. The evaluation process included comparisons with leading open-source thinking models and closed-source APIs, including GPT-OSS-120B(medium), Qwen3-32B-Thinking, Seed-OSS-36B-Instruct, and Gemini-2.5-Flash.

Mathematical Competition Performance: Ring-flash-2.0 exhibits outstanding performance in mathematical reasoning tasks, particularly in competitions such as AIME 25 and Omni-MATH. These benchmarks test the model’s ability to solve complex mathematical problems that require multi-step reasoning, pattern recognition, and advanced mathematical knowledge.

Code Generation Excellence: The model demonstrates superior capabilities in code generation tasks, as evidenced by its performance on LiveCodeBench and CodeForce-Elo benchmarks. These evaluations test the model’s ability to understand programming concepts, implement algorithms, and solve computational problems across various programming languages and complexity levels.

Logical Reasoning Capabilities: In logical reasoning evaluations, particularly the ARC-Prize benchmark, Ring-flash-2.0 showcases advanced abstract reasoning abilities. The model can identify patterns, make logical inferences, and solve problems that require sophisticated cognitive processing.

Specialized Domain Performance

Beyond general reasoning tasks, Ring-flash-2.0 demonstrates strong competitiveness in specialized domains:

Scientific and Medical Reasoning: The model shows impressive performance in GPQA-Diamond and HealthBench evaluations, demonstrating its ability to handle complex scientific concepts and medical reasoning tasks. This capability makes Ring-flash-2.0 valuable for specialized applications in healthcare, research, and scientific analysis.

Creative Writing Capabilities: Surprisingly, despite being primarily designed for complex reasoning, Ring-flash-2.0 outperforms all compared models in creative writing tasks (Creative Writing v3). This unexpected strength demonstrates the model’s versatility and suggests that advanced reasoning capabilities can enhance creative expression. The model matches the creative capability of its “twin brother,” the non-thinking model Ling-flash-2.0.

Deployment and Implementation

Flexible Deployment Options

Ring-flash-2.0 supports multiple deployment frameworks, providing flexibility for different use cases and infrastructure requirements. The model can be deployed using several popular inference engines, each offering specific advantages for different scenarios.

Hugging Face Transformers Integration: The model provides seamless integration with the Hugging Face ecosystem, enabling easy adoption for developers familiar with the transformers library. The straightforward API allows for quick implementation and testing of the model’s capabilities.

vLLM Deployment: For high-performance inference scenarios, Ring-flash-2.0 supports deployment through vLLM, offering both offline batched inference and online API services. The vLLM integration enables optimal resource utilization and supports advanced features like tensor parallelism for distributed inference.

SGLang Support: The model also supports deployment through SGLang, providing additional options for specialized inference requirements. SGLang support includes both BF16 and FP8 precision options, allowing for optimized performance based on hardware capabilities and accuracy requirements.

Performance Optimization Features

Ring-flash-2.0 includes several advanced features for deployment optimization:

Context Length Extension: The model supports long context handling through YaRN (Yet another RoPE extensioN) scaling, enabling processing of extended sequences while maintaining performance. This capability is particularly valuable for applications requiring analysis of long documents or extended conversations.

Speculative Decoding: For base model deployments, Ring-flash-2.0 supports speculative decoding through the NEXTN algorithm, further enhancing inference speed for suitable use cases.

Technical Innovation and Future Implications

Breakthrough Significance

Ring-flash-2.0 represents a significant advancement in the field of thinking models, addressing several critical challenges that have limited the practical deployment of advanced reasoning systems. The successful development and implementation of the IcePop algorithm provides a template for future MoE model development and training optimization.

The model’s ability to achieve leading performance while maintaining exceptional efficiency demonstrates that sophisticated reasoning capabilities need not come at the cost of computational accessibility. This breakthrough has important implications for democratizing access to advanced AI reasoning capabilities across different scales of deployment.

Industry Impact

The innovations introduced in Ring-flash-2.0 extend beyond the model itself, contributing to the broader understanding of efficient training methodologies for large-scale models. The IcePop algorithm’s approach to addressing training-inference gaps provides valuable insights for the development of future AI systems.

The model’s success in combining high performance with efficiency creates new possibilities for deploying advanced reasoning capabilities in resource-constrained environments. This advancement could accelerate the adoption of thinking models in practical applications where computational resources are limited.

Conclusion

Ring-flash-2.0 represents a remarkable achievement in the evolution of thinking models, successfully combining breakthrough performance with exceptional efficiency. Through the innovative IcePop algorithm and sophisticated multi-stage training pipeline, the model addresses fundamental challenges in MoE architecture training while delivering leading results across diverse reasoning tasks.

The model’s ability to activate only 6.1 billion parameters while achieving performance comparable to much larger dense models demonstrates the power of intelligent architectural design. With inference speeds exceeding 200 tokens per second on modest hardware configurations, Ring-flash-2.0 makes advanced reasoning capabilities more accessible for real-world applications.

The comprehensive evaluation results across mathematical competition, code generation, logical reasoning, and creative writing tasks establish Ring-flash-2.0 as a versatile and powerful tool for complex problem-solving. The model’s unexpected strength in creative tasks further demonstrates the interconnected nature of different cognitive capabilities.

As the field of AI continues to evolve, Ring-flash-2.0 sets new standards for what is possible in terms of combining performance, efficiency, and practical deployability. The innovations introduced in this model will likely influence future developments in thinking models and contribute to the broader democratization of advanced AI capabilities.

For organizations and researchers seeking to implement advanced reasoning capabilities, Ring-flash-2.0 offers a compelling combination of performance, efficiency, and accessibility that makes it an excellent choice for a wide range of applications. The model’s open-source availability and comprehensive deployment support further enhance its value for the AI community.

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