AI Glossary
What is Test-Time Scaling?
Overview
Test-time scaling improves an AI system's answer by spending more computation during inference, for example through longer reasoning, multiple candidate solutions, search, or verification. It matters because models can often solve harder tasks without retraining when the system allocates compute adaptively and uses reliable methods to select or check the result.
Why it matters
Test-time scaling shifts part of model performance from training into the inference system, creating new tradeoffs among accuracy, latency, and cost.
Where it appears in AI research
- Reasoning model evaluations
- Agent planning and search systems
- Process reward and verification research
- Inference cost and latency analysis
Related terms
Related DeepSignal articles
SLPO: Scaling Latent Reasoning via a Surrogate Policy
SLPO introduces Surrogate Latent Policy Optimization to enhance outcome-reward reinforcement learning for autoregressive latent reasoners, improving Pass@$k$ performance and enabling longer computations for complex tasks. This method addresses the limitations of latent reasoning by providing a trajectory-level credit assignment and a correctness-supervised stopping mechanism, leading to better efficiency in reasoning tasks.