Scalable Oversight

Training and evaluation methods that remain reliable as model capabilities outrun direct human evaluation.

Technical Safety·Exploring·Last reviewed May 1, 2026

This page is a stub. I’ve marked the territory but haven’t written my views here yet. The headings below are placeholders — the actual beliefs, uncertainties, and evidence are still in my notes. If you want my current take on this topic before it lands here, get in touch.

Where I currently stand

<Headline view: debate, weak-to-strong, recursive reward modelling, deliberative alignment — which of these you take seriously, and why. Likely a hedged view since this is a research-direction question rather than a concrete-claims question.>

Current beliefs

  • Weak-to-strong generalisation is currently more useful as a measurement tool than as an alignment method. ~XX%<why>.
  • <Claim about debate / RRM / DA.> ~XX%<why>.
  • <Claim about how oversight methods compose with control.> ~XX%<why>.

Uncertainties

  • Which oversight methods survive when the gap between supervisor and supervised becomes large? Why it matters: the whole agenda is conditioned on this being non-empty.
  • Is "scalable oversight" the right framing, or should the field move to "scalable specification"? Why it matters: changes which research bets pay off.

What would update me

  • A clean empirical demonstration that one oversight method survives a meaningful capability gap on a non-toy task.
  • Replication failures of high-profile scalable-oversight results.

Recent reading

  • <date><title><takeaway>.

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