Triplet-Block Diffusion RWKV
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| Authors | Ke Lin et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2605.25969 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and discrete diffusion models each address these weaknesses, their integration remains inherently inconsistent: diffusion requires bidirectional attention, while causal models are unidirectional. To unify these architectures, we propose B^3D-RWKV, a diffusion RWKV variant that integrates the model's O(L) inference efficiency with parallel, bidirectional discrete-diffusion through a triplet-block layout method. B^3D-RWKV-7.2B reaches comparable accuracy on an 8-task suite versus existing models while significantly outperforming baselines in decoding throughput with an average of 1.6times speedup.
Engineering Breakdown
The Problem
Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost.
The Approach
To unify these architectures, we propose B^3D-RWKV, a diffusion RWKV variant that integrates the model's O(L) inference efficiency with parallel, bidirectional discrete-diffusion through a triplet-block layout method.
Key Results
B^3D-RWKV-7.2B reaches comparable accuracy on an 8-task suite versus existing models while significantly outperforming baselines in decoding throughput with an average of 1.6times speedup.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Tripletblock
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