Stanford's Prefix Sliding speeds up long reasoning by 3x
omarsar0 · x · 2026-08-31
Stanford researchers introduced Prefix Sliding, a new method to optimize test-time compute for long reasoning chains (CoT).
- Problem: Long reasoning traces require full attention, keeping the entire history in memory. This makes the hardest problems (which need the most thinking) the most expensive to run.
- Insight: Intermediate tokens steadily lose importance as the model continues reasoning.
- Solution: Prefix Sliding retains the prefix (instructions and available tools) and a sliding window of the last few thousand tokens, discarding everything in between. This caps total memory usage regardless of reasoning length.
- Results: Achieves a 3x speedup on existing models without any training, while matching full-attention performance.
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