Co-designing System-1 and System-2 improves AGI scaling efficiency
Original titleDoes Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?
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- Hugging Face
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Summary
The authors argue that AGI scaling should co-design a lightweight System-1 predictor with resource-intensive System-2, where System-1 evaluates candidates before System-2 executes. They propose that execution compute can fall by a factor approaching the inverse of utility-relevant interaction density, making informative compute a better scaling metric than total compute.
Original excerpt
Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study conditional interaction, a predict--gate--execute--update loop: System-1 estimates each candidate's utility, System-2 executes those admitted by an adaptive threshold, and the outcomes update System-1. The loop spans training, test-time reasoning, adaptive decision-making, and agentic tool use, with utility and cost defined by the application. Under ideal screening and equal execution costs, execution compute falls by a factor approaching 1/ρ_u, where ρ_u is the density of utility-relevant interactions (cost-weighted when costs differ); prediction errors, exploration, and distribution shift reduce the saving. We bound selection regret by predictor error and the utility margin. These results motivate our central hypothesis: ρ_u is a measurable coordinate of scaling efficiency and makes informative compute a better description of scaling than total compute.