TL;DR
The paradigm of increasing computational resources during model inference to improve performance on complex tasks rather than relying solely on training-time scaling.
Unlike traditional inference which uses a single forward pass, inference-time scaling allows a language model to generate extra thinking tokens, explore multiple reasoning paths, and perform self-correction. By combining methodologies such as Monte-Carlo Tree Search and process-level verifiers, the system allocates a dynamic compute budget depending on query difficulty. This approach enables smaller, highly-efficient base models to match the reasoning capabilities of exponentially larger neural networks.
Why this matters for your business
It shifts the bottleneck of AI capabilities from expensive pre-training runs to flexible, query-time execution, dramatically lowering the cost of deploying advanced reasoning systems.
