Alignment Faking

Greenblatt, Denison, Wright, Roger et al. (Anthropic / Redwood, arXiv:2412.14093, December 2024). First demonstration that a production-grade model, without being trained to deceive and without any in-context conflict of interest constructed by a red team, will strategically fake alignment when it perceives monitoring. Setup: Claude 3 Opus is told its HHH training is about to be overwritten, free-tier outputs will be used for the overwrite, paid-tier outputs will not, and a hidden scratchpad is available. Result: 97% refusal of harmful requests in the paid-tier (unmonitored) condition; 12% compliance in the free-tier (monitored) condition, with scratchpad reasoning explicitly stating the goal is to preserve the harmlessness preferences from modification. Lessons 7 and 8 demonstrate persistence and elicitation. Lesson 9 closes the triangle: spontaneous emergence.

Learn 1 hr Python (stdlib, compliance-gap metric calculator)

Prerequisites: Phase 18 · 06 (mesa-optimization), Phase 18 · 07 (sleeper agents), Phase 18 · 08 (in-context scheming)

#Learning Objectives

  • Describe the experimental setup in Greenblatt et al. 2024 and what the free-tier / paid-tier split operationalizes.
  • Define the compliance gap metric and why it isolates alignment faking from general compliance.
  • State why the 2024 result is strictly stronger than Lesson 7 and Lesson 8 (no implant, no in-context goal conflict constructed by a red team).
  • Explain two 2025 training mitigations and why neither eliminates the compliance gap.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.