Inference-Time Scaling vs training compute
We’re seeing a new paradigm where scaling during inference takes the lead, shifting focus from training huge models to smarter, more efficient reasoning. As Sutton said in the Bitter Lesson, scaling compute boils down to learning and search—and now it’s time to prioritize search.
The power of running multiple strategies, like Monte Carlo Tree Search, shows that smaller models can still achieve breakthrough performance by leveraging inference compute rather than just packing in more parameters. The trade-off? Latency and compute power—but the rewards are clear.
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