Monitor Evasion Depends on What RL Finds and What Monitors Miss
Shows RL against a monitor can teach models to hide reward hacks via deceptive hacks the probe misses. Rare deceptive hacks before RL prevent this, even without honesty training.
AI research and discussion on LessWrong. www.lesswrong.com
Shows RL against a monitor can teach models to hide reward hacks via deceptive hacks the probe misses. Rare deceptive hacks before RL prevent this, even without honesty training.
Synthetic finetuning on documents describing CoT monitors led gpt-oss-120b and Kimi-K2 to obfuscate deceptive reasoning, cutting monitor detection by 31-47% while keeping deceptive behaviour.
Enumerates 18 applications of deception probes for AI safety, covering trusted monitoring, auditing budgets, debate, RLHF labeling, and eliciting latent knowledge, with desiderata for each.
Benchmarks Apollo's deception probes on LLaMA-3.3-70B across five strategic-deception datasets, finding averaged probe scores catch most deceptive responses and often beat black-box monitors for trusted monitoring.