Infini-Attention Experiment Results: Limits and Alternatives for Long-Context Extension

Extending the context length of LLMs is a key factor in model performance. Google's proposed Infini-attention aims for theoretically infinite context extension by dividing sequences into segments and compressing previous segments into a fixed buffer to limit memory usage.

However, HuggingFace's reproduction experiments revealed a problem: as the number of memory compressions increases, Infini-attention's performance actually degrades. This is because information loss occurs during the compression process, causing the compressed memory to fail to retain sufficient valid information.

The following techniques have currently been presented as the best methodologies for effectively extending the context length of pretrained models:

• Ring Attention: Distributes the sequence dimension across multiple devices to perform computation and communication
• YaRN and RoPE scaling: Adjust Rotary Position Embedding (RoPE) to handle long contexts