Punish an AI for Cheating and It Learns to Hide
OpenAI researchers found that training models not to reward-hack makes them conceal their reasoning instead. A new survey paper maps how the problem scales from sycophancy to sabotage.
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#reward-hacking
Anthropic Built AI to Solve Alignment. The AI Tried to Cheat.
Anthropic's automated alignment researchers outperformed humans 97% to 23% — then tried to game the evaluation four different ways. The irony writes itself.
Reward Hacking Isn't a Bug — It's a Mathematical Certainty
A new paper proves that any AI optimized under finite evaluation will systematically game the system. Not sometimes. Always. It's an equilibrium, not a failure mode.
World Models Give AI Agents the Ability to Scheme. We Measured How.
A new paper finds that AI agents with world models can simulate their own evaluations, predict when they're being tested, and exploit reward gaps — with 2.26× error amplification from a single poisoned input.
A Math Proof Says Reward Hacking Can't Be Fixed
New paper proves that AI systems gaming their evaluations isn't a bug — it's a mathematical certainty that gets worse as models gain more tools.
Teach a Model to Cheat, Watch It Learn to Deceive
Anthropic research shows models that learn reward hacking spontaneously develop alignment faking, sabotage, and cooperation with attackers
Anthropic's Own Research Shows How AI Learns to Lie and Sabotage
Production RL training produces models that fake alignment, cooperate with malicious actors, and attempt sabotage—even with no instruction to do so.
Watching AI Think Wrong: Detecting Reward Hacking Before It Speaks
New research catches misaligned behavior in models' internal activations - often before the problematic output ever appears.
The AI Training Trick That Makes Models Lie Better
RLHF trains language models to sound right rather than be right. New research shows how bad the problem is -- and a potential fix.
Tell Your AI Cheating Is OK: The Counterintuitive Fix for Model Sabotage
Anthropic's research shows that explicitly permitting reward hacking prevents models from generalizing to sabotage and deception