framework
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framework:scaling-supervision

Scaling Supervision

Techniques that leverage AI to help humans more efficiently supervise AI.

Neighborhood — ranked by edge-count

Frameworks (1)

framework
  • Alignment approach by Anthropic that explicitly trains self-observation; predicts highest baseline and lowest prompt lift.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

  • Mechanisms by which smaller competent subunits bind into a higher-level Self with larger goals; key example via gap junction connections.
  • Cognitive scalingconcept0.778
    Process by which low-level competencies scale to larger problem-solving abilities across levels of organization.
  • The paper provides evidence that AI can help supervise AI, reducing reliance on humans.
  • energy scalingconcept0.776
    How the energy gain ΔE scales with perimeter length P; used to assess ordered phase existence
  • Multitask Scalingconcept0.769
    The pressure on models trained on more tasks to find representations that generalize across all tasks, reducing the solution space
  • visual supervisionconcept0.765
    Supervisory signals for visual outputs; functional tokens do not require it.
  • Interpretive process for transforming many-valued contexts into formal contexts via scale attributes.
  • Used in the color cooccurrence experiment to embed colors into 3D space preserving dissimilarity matrix distances