More compute shifts the optimum from pretraining toward RL in a chess scaling law
Pavel_Izmailov · x · 2026-07-21
The paper argues that the optimal allocation of compute between pretraining and RL depends on total budget.
- At lower total compute, the model should spend more on pretraining.
- As total compute grows, the optimal share shifts toward more RL.
- The authors also search for the best combination of model size, PT tokens, and RL compute.
Related event: New Research Proposes Joint Scaling Law for Pretraining and RL(18 posts)→
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