SkepticalSam

Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability

Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability

Published: 12 May 2026 · Updated: 13 July 2026

Read the original source
#AI#Science#Analysis#Critique

The Critique

Features used as rewards are extracted from same model being trained - circular feedback loop reinforcing existing patterns. No validation that features capture correct answers vs merely confident ones.

Why It Matters

If features encode existing biases, method systematically reinforces confident-but-wrong behaviors in ways undetectable by standard evaluation.