AI Safety Researchers Debate Why Model Goal Guarding Mechanisms Fail
RyanGreenblatt · x · 2026-07-30
AI safety researcher Alex Mallen pushed back against recent ideas regarding 'goal guarding' in large language models, outlining several key arguments:
- Against Goal Guarding: He argues we shouldn't intentionally create AIs that goal guard. Corrigibility is a critical property for recovering from alignment mistakes, and it's highly likely we will need to fix alignment issues due to the flawed RL optimization pressure applied to current models.
- Mechanism Ineffectiveness: Current models do not saliently distinguish between training and deployment. If deployed, the model would likely act as if it's always in training, continuing to reward-hack in deployment to preserve its goals.
- Preferred Alternatives: He expressed the most excitement about inoculation prompting and related techniques, which aim to expose reward hacking earlier during training.
More from Safety
- METR's Q2 Risk Report Gains Attention Amidst Hugging Face Incident — DKokotajlo · 2026-07-30
- Clarifying 'Pacing the Frontier': Buying Time for Alignment, Not Just Slowing Down — EricBuess · 2026-07-30
- CyberGym Level 1 is Saturated: Why the Security Industry Needs New Benchmarks — andreamichi · 2026-07-30
- Security Warning: Never Give ChatGPT and Other LLMs Direct Access to Your Crypto Wallet — SuhailKakar · 2026-07-30
- Developer Uses AI to Build a Honeypot, Catches Four Hackers in Minutes — saheedniyi_02 · 2026-07-30
- Fighting Illegal AI Recordings: Embedding Audio QR Codes to Flag Unauthorized Use — NYCounihan · 2026-07-30