Ring-flash-linear-2.0: Efficient Transformer

• Ring-flash-linear-2.0 is a transformer model that employs a hybrid attention mechanism, combining linear and softmax modules to enable efficient long-context reasoning.
• It utilizes a sparsely activated 104B-parameter architecture with a Mixture-of-Experts strategy to significantly reduce training and inference costs.
• Custom FP8 optimizations via the Linghe library and polymer-inspired design insights further enhance computational efficiency and stability on complex reasoning tasks.

Ring-flash-linear-2.0 refers to a large-scale transformer-based neural network model developed in the context of efficient long-context reasoning. It embodies a hybrid attention architecture that strategically combines linear and softmax attention mechanisms. With 104 billion total parameters and 6.1 billion non-embedding activations, Ring-flash-linear-2.0 demonstrates substantial reductions in inference and training cost relative to earlier models while maintaining state-of-the-art performance on a range of complex reasoning tasks, notably through the application of a self-developed high-performance FP8 operator library termed “linghe” (Team et al., 22 Oct 2025). Furthermore, the “Ring-flash-linear-2.0” concept draws on mechanistic insights from polymer science, specifically mappings between topological constraint relaxation in polymer blends and information flow within neural architectures (Vigil et al., 23 Apr 2024).

1. Hybrid Attention Architecture

Ring-flash-linear-2.0 employs a hybrid transformer architecture, in which model layers are organized into groups. Each group consists of several (M) Lightning Attention (linear attention) modules, followed by a single softmax attention block (frequently implemented as Grouped Query Attention, GQA). Linear attention reduces computational and memory complexity from $O(n^2 d)$ to $O(nd^2)$, where $n$ is sequence length and $d$ is embedding dimensionality.

• Linear Attention (“Lightning Attention”): Reformulates the attention computation as two stages: accumulation of key-value (KV) pairs and their projection onto the query. The operation $O=Q (K^\top V)$ is central, where $Q, K, V \in \mathbb{R}^{n\times d}$.
• Softmax Attention Blocks: Intermittently incorporated for their superior ability to model global token dependencies, crucial in retrieval and complex reasoning tasks.
• Architectural Integration: Most computation occurs in linear attention layers, capitalizing on reduced cost, while the inclusion of softmax blocks at regular intervals preserves high-capacity, token-level modeling.

This organization efficiently compresses memory and compute requirements during both training and inference, enabling effective scaling to long-context inputs.

2. Parameterization and Mixture-of-Experts Activation

Ring-flash-linear-2.0 contains 104B total parameters, with 7.4B activated per forward pass (6.1B non-embedding). The architecture adopts a sparse Mixture-of-Experts (MoE) paradigm, enforcing a 1/32 activation ratio. Consequently, each inference step utilizes only a low fraction of the full parameter set, facilitating scalability and efficient capacity utilization, particularly for long input sequences with complex interdependencies.

Model Variant	Total Parameters	Activated Parameters	Non-Embedding Activations
Ring-mini-linear-2.0	16B	957M	Not stated
Ring-flash-linear-2.0	104B	7.4B	6.1B

The allocation of a large, but selectively activated, parameter pool provides representational power essential for preserving performance in reasoning benchmarks, while restricting runtime costs.

3. Computational and Memory Efficiency

Ring-flash-linear-2.0 achieves major improvements in computational efficiency:

• Inference Cost Reductions:
  • $\approx$1/10 the inference cost of a 32B parameter dense model.
  • Over 50% inference cost reduction relative to the original Ring series.
• Mechanisms for Efficiency:
  • Lightning Attention enables linearly-scaling memory and compute via accumulated KV caches, achieving constant key-value storage per token.
  • The hybrid pattern minimizes I/O overhead by decreasing softmax attention frequency.
  • Kernel partitioning and specialized fusion operations minimize redundant memory access and further reduce per-step latency.

A key mathematical recurrence underlying the KV cache in linear attention is

$$\begin{aligned} kv_0 &= 0 \in \mathbb{R}^{d \times d} \ kv_t &= \lambda \cdot kv_{t-1} + (k_t)^\top v_t \ o_t &= q_t (kv_t) \end{aligned}$$

where $\lambda$ is a decay factor. This formulation ensures stable, efficient information propagation over long input sequences.

4. FP8 Optimization and the Linghe Library

The integration of a custom FP8 operator library (“linghe”) is central to the observed efficiency:

• FP8 Mixed Precision: GEMM and related operations execute directly in FP8, providing lower memory bandwidth and compute requirements compared to BF16/FP32 without sacrificing numerical stability.
• Kernel and Quantization Fusion: Operations such as SiLU activation and quantization are fused to minimize memory traffic and procedural overhead.
• State-Aware Recomputation: Differentiation between regular and recomputation passes ensures that recomputation is restricted to only necessary portions of the network, vital during reinforcement learning phases.
• Training–Inference Alignment: The library is designed to minimize discrepancies between training and inference computational paths, supporting long, stable episodes in RL-based fine-tuning.

The net result is a 50% improvement in overall training efficiency, as measured against prior implementations without this level of FP8 and kernel fusion.

5. Empirical Performance and Benchmarking

Ring-flash-linear-2.0 maintains state-of-the-art performance across a wide array of challenging reasoning and agent-based benchmarks, despite its comparatively low activated parameter count. Benchmarks include:

• Mathematical Reasoning: AIME’24, AIME’25, OlympiadBench, CNMO’24, LiveMathBench, TheoremQA
• Code Synthesis and Agent Tasks: Humaneval+, MBPP+, LiveCodeBench, CodeForces, Spider, BFCL_Live
• General and Logical Reasoning: GPQA-Diamond, SciBench, DROP, MuSR, Multi_LogiEval

Performance gains are attributed to the effective synergy of hybrid attention, MoE parameterization, and system-level optimizations enabling tractable operation on very long contexts.

6. Conceptual and Scientific Implications

Mechanistic analogies are drawn in the literature between topological relaxation in polymer blends and information propagation in hybrid transformer architectures. In ring-linear polymer blends, relaxation of topological constraints—mediated by threading and reptation—can be quantitatively modeled through the Gauss linking integral and relevant scaling laws

$$\tau_{d,R} = \tau_{d,R_0} \left[ 1 + \left(\frac{N_L}{N_{L,c}} \right)^{3.4/\alpha} \right]^\alpha,$$

where the relaxation time $\tau_{d,R}$ links to linear chain length $N_L$ (Vigil et al., 23 Apr 2024). This suggests potential analogues in architectural design for dynamically adjusting information bottlenecks and relaxation times within deep networks. For Ring-flash-linear-2.0, this scientific framework underpins potential future directions in tailoring model response times and topological memory mechanisms.

7. Impact and Design Strategies

The Ring-flash-linear-2.0 framework provides a roadmap for further scaling and specialization of long-context reasoning models:

• By systematically controlling the ratio of linear and softmax attention layers, model designers can balance efficiency and expressive power for target domains and workloads.
• The direct alignment of training and inference computational graphs, particularly under reinforcement learning regimes, permits more stable and effective optimization—crucial for domains with long-range credit assignment or where output stability over long episodes is essential.
• Insights from polymer analogies inform the design of models with tunable memory and relaxation times, potentially enabling adaptive response to varied sequential dependencies.

In summary, Ring-flash-linear-2.0 advances the state of efficient long-context transformers through architectural, computational, and algorithmic innovations, all grounded in systematic empirical and mathematical investigation (Team et al., 22 Oct 2025, Vigil et al., 23 Apr 2024).