Test-Time Scaling

Opening — Why this matters now Large Language Models are no longer compute-bound at training time. They are inference-bound at deployment time. The last year has made this painfully clear. Frontier reasoning models increasingly win benchmarks not by being smarter, but by thinking more: longer chains-of-thought, more samples, more retries, more votes. The result is an arms race in test-time scaling—512 samples here, best-of-20 there—where accuracy inches upward while token bills explode. ...

Opening — Why this matters now The industry has been quietly converging on an uncomfortable realization: raw model scaling is running out of low-hanging fruit. Training bigger models still works, but the marginal cost curve has become brutally steep. Meanwhile, real-world deployments increasingly care about inference economics—latency, throughput, and cost per correct answer—not leaderboard bravado. ...