Scaling laws matter more than ever in the RSI era: small models as proxies
ChengleiSi · x · 2026-11-09
A viewpoint thread arguing that understanding scaling laws becomes critical in the era of recursive self-improvement (RSI): if conclusions from small models can be reliably transferred as proxies to large models, AI itself could explore better architectures, data recipes, and infra on small models— even packaged as RL training tasks and benchmarks—since verification costs at small scale become affordable as compute grows.
The quoted discussion adds context: current public scaling-law claims are highly inconsistent and too data-dependent; below a certain pre-training token count, unified scaling laws can hardly be discussed independent of the data, motivating experiments under a unified data setup.
More from AGI Musings
- AI safety's Miyazaki moment: young researchers only study Claude, converge on same local minima — JacquesThibs · 2026-11-09
- Security pro slams stale 'patch and unplug' advice in the era of AI cyber superweapons — HankYeomans · 2026-11-09
- From chihuahua vs. muffin to Snell's-law raccoons: a decade of AI expectations — korymath · 2026-11-09
- Multi-Agent AI Safety Via Identity, Reputation and Collective Incentives — Competitive_Travel16 · 2026-11-09
- Larry Page predicts 99.999% of compute will end up in orbit, citing US data center regulation — elonmusk · 2026-11-09
- Debate over valuing human life in numbers splits AI ethics circles around Gebru — austinc3301 · 2026-11-09