Prefix Sliding for Efficient Test-Time Scaling

Researchers introduced Prefix Sliding, which retains a fixed prefix and recent context while discarding stale reasoning tokens during inference.

The method reduces memory use and can make existing language models roughly 3× faster on long reasoning tasks without retraining.

Combined with reinforcement learning, it can support substantially longer reasoning traces at lower test-time cost.