Scale Up, Not Search: How RLHF Compute Should Be Spent
A systematic sweep of a fixed FLOP budget shows that boosting model size yields the biggest gains for RL post‑training, while extra search steps, longer learning, or more human feedback provide only marginal improvements. Frontier labs can cut costs by prioritising scaling over iterative refinement.