Stanford Prefix Sliding Speeds AI Reasoning 3x to Scale Beyond 100K Tokens

Researchers at Stanford University developed a method to reduce the computational cost of long-chain-of-thought inference by discarding unimportant intermediate tokens from memory. The technique, called Prefix Sliding, maintains the original prompt and a sliding window of recent reasoning, allowing existing models to run up to 3x faster while matching full-attention performance without requiring retraining.

The approach addresses the tendency for inference costs and memory usage to grow as reasoning chains lengthen. When combined with reinforcement learning fine-tuning, Prefix Sliding allows AI agents to scale reasoning beyond 100,000 tokens by capping total memory regardless of the chain length.