AI：推理时代已至

As the scaling of pretraining is reaching a plateau of diminishing returns, model inference is quickly becoming an important driver for model performance. Today, test-time compute scaling offers a new, exciting avenue to increase model performance beyond what can be achieved with training, and test-time compute techniques cover a fertile area for many more breakthroughs in AI. Innovations using ensemble methods, iterative refinement, repeated sampling, retrieval augmentation, chain-of-thought reasoning, search, and agentic ensembles are already yielding improvements in model quality performance and offer additional opportunities for future growth.