Prefix Sliding for efficient test-time scaling
Prefix Sliding drops stale reasoning tokens while retaining instructions and recent work, capping memory during long inference and reportedly making existing models 3× faster without training.
Prefix Sliding retains the instruction-and-tools prefix plus a window of the latest few thousand tokens, discarding older intermediate reasoning. This caps memory independently of reasoning length and made existing models **3× faster without training** while maintaining performance.
Builders operating long-running agents should distinguish durable context from transient reasoning. Keeping stable instructions and current work may be more efficient than retaining every intermediate token; reinforcement learning with the method enabled traces beyond **100,000 tokens**.
Prefix Sliding retains the instruction-and-tools prefix plus a window of the latest few thousand tokens, discarding older intermediate reasoning. This caps memory independently of reasoning length and made existing models **3× faster without training** while maintaining performance. Builders operating long-running agents should distinguish durable context from transient reasoning. Keeping stable instructions and current work may be more efficient than retaining every intermediate token; reinforcement learning with the method enabled traces beyond **100,000 tokens**. The claims come from a new paper, and the supplied material does not name the evaluated models, workloads, or deployment costs. Its ablations beat summarization and a vanilla sliding window, but broader generality remains open.