Compact mode
Hyena vs RetNet
Table of content
Core Classification Comparison
Algorithm Family 🏗️
The fundamental category or family this algorithm belongs toBoth*- Neural Networks
Industry Relevance Comparison
Modern Relevance Score 🚀
Current importance and adoption level in 2025 machine learning landscapeBoth*- 9
Basic Information Comparison
Purpose 🎯
Primary use case or application purpose of the algorithmBoth*- Natural Language Processing
Known For ⭐
Distinctive feature that makes this algorithm stand outHyena- Subquadratic Scaling
RetNet- Linear Scaling Efficiency
Historical Information Comparison
Performance Metrics Comparison
Accuracy 🎯
Overall prediction accuracy and reliability of the algorithmHyena- 8Overall prediction accuracy and reliability of the algorithm (25%)
RetNet- 8.5Overall prediction accuracy and reliability of the algorithm (25%)
Application Domain Comparison
Modern Applications 🚀
Current real-world applications where the algorithm excels in 2025Both*- Large Language Models
HyenaRetNet- Natural Language Processing
Technical Characteristics Comparison
Complexity Score 🧠
Algorithmic complexity rating on implementation and understanding difficultyHyena- 7Algorithmic complexity rating on implementation and understanding difficulty (25%)
RetNet- 8Algorithmic complexity rating on implementation and understanding difficulty (25%)
Computational Complexity ⚡
How computationally intensive the algorithm is to train and runBoth*- Medium
Computational Complexity Type 🔧
Classification of the algorithm's computational requirementsBoth*- Linear
Key Innovation 💡
The primary breakthrough or novel contribution this algorithm introducesHyena- Convolutional Attention
RetNet- Retention Mechanism
Evaluation Comparison
Facts Comparison
Interesting Fact 🤓
Fascinating trivia or lesser-known information about the algorithmHyena- Uses biological inspiration from hyena communication patterns
RetNet- Achieves similar performance to Transformers with significantly better efficiency
Alternatives to Hyena
RWKV
Known for Linear Scaling Attention🔧 is easier to implement than RetNet
⚡ learns faster than RetNet
State Space Models V3
Known for Long Sequence Processing🔧 is easier to implement than RetNet
⚡ learns faster than RetNet
Sparse Mixture Of Experts V3
Known for Efficient Large-Scale Modeling🔧 is easier to implement than RetNet
SVD-Enhanced Transformers
Known for Mathematical Reasoning🔧 is easier to implement than RetNet
MambaByte
Known for Efficient Long Sequences🔧 is easier to implement than RetNet
⚡ learns faster than RetNet
S4
Known for Long Sequence Modeling🔧 is easier to implement than RetNet
Hierarchical Attention Networks
Known for Hierarchical Text Understanding🔧 is easier to implement than RetNet
FlashAttention 2
Known for Memory Efficiency⚡ learns faster than RetNet
📊 is more effective on large data than RetNet
🏢 is more adopted than RetNet
📈 is more scalable than RetNet
RoPE Scaling
Known for Long Context Handling🔧 is easier to implement than RetNet