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Foundation Lab

Sandwiching Evaluations

Makes scalable oversight empirically testable today

Concept 94 of 100Scaling & AlignmentPhase 12
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#94SandwichScaling & Alignment
key equation
Score=PH+A−PHPE−PH\text{Score} = \frac{P_{H+A} - P_H}{P_E - P_H}
Reading map and next steps

Selected Foundation Object

Keep the equation fixed; move through the evidence.

Concept 94 of 100SandwichScaling & Alignment / Phase 12: Advanced alignment & safety research
Current question

Bottom = unaided human, top = expert, middle = AI-assisted human

Score=PH+A−PHPE−PH\text{Score} = \frac{P_{H+A} - P_H}{P_E - P_H}
PredictionCommit before tracing the equation.

Ask what should change under a concrete input, then trace that expectation through the equation.

EvidenceCompare the equation and source.

Use the key equation and canonical papers as the available witnesses, without implying that a runnable panel exists.

InvariantName what survives notation changes.

The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.

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Why It Matters for Modern Models

  • Makes scalable oversight empirically testable today
  • Choose tasks where experts can judge, non-experts struggle
  • Proxy for future "smart model oversight" capabilities

What Tutorials Skip

What is still poorly explained in textbooks and papers:

  • Bottom = unaided human, top = expert, middle = AI-assisted human
  • Tests: can weaker oversight + AI match stronger oversight?
  • Foundational benchmark for alignment research progress

Visualization Status

Core Math (Optional Deep Dive)

If you want intuition first, start with the key equation and cited sources. Come back here for the full walkthrough.

Key Equation
Score=PH+A−PHPE−PH\text{Score} = \frac{P_{H+A} - P_H}{P_E - P_H}

Sandwich score measures AI-assisted oversight:

SandwichScore=PH+A−PHPE−PH\text{SandwichScore} = \frac{P_{H+A} - P_H}{P_E - P_H}
  • PHP_H: non-expert performance
  • PH+AP_{H+A}: non-expert + AI assistance
  • PEP_E: expert performance

Score = 1.0 means assisted non-expert matches expert.

Canonical Papers

Measuring Progress on Scalable Oversight for Large Language Models

Bowman et al.2022Anthropic
Read paper →

Connections

Prerequisites

⚖RLHF🔄IDA

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