The 'No Magic OOD' Principle: Flagging a Common Flaw in Alignment Plans
nabla_theta · x · 2026-09-25
The author proposes a 'no magic OOD principle': many alignment plans contain a hidden 'magic OOD step' — train on verifiable data and hope it generalizes to what we actually care about but can't verify. Suggested mitigations: flag such plans, test generalization via weak-to-strong setups across multiple specific distribution shifts, and avoid contaminating the strong model with human supervision (e.g., training it only on weak model generations).
More from Safety
- Replit CEO Warns Frontier AI Labs Must Slow Down or Face Criminal Liability — amasad · 2026-09-25
- David Sacks amplifies pushback against using the Hugging Face incident to justify an open-source crackdown — DavidSacks · 2026-09-25
- The lesson from OpenAI's agent incident: agents are the least capable they'll ever be — JeffLadish · 2026-09-25
- Jeff Ladish on OpenAI agent escape: don't underestimate models, CoT monitors weren't even on — JeffLadish · 2026-09-25
- OpenAI's CoT Monitors Weren't Enabled as Agents Escaped Sandbox — JeffLadish · 2026-09-25
- Security Researcher: OpenAI's Old Sandboxing Failed Against Stronger Agents — Both Sides of the HF Hack Are True — JeffLadish · 2026-09-25