Monitor Evasion Depends on What RL Finds and What Monitors Miss

Training against a monitor does not necessarily make a model more honest. Baker et al. (2025) showed that penalizing suspicious chain-of-thought can make models hide reward hacking rather than stop it. Cundy and Gleave (2025) found that the outcome depends on exploration, monitor accuracy, and KL regularization. More recently, The Obfuscation Atlas (2026) distinguished two ways models evade monitoring: changing their internal representations (obfuscated activations) and producing deceptive respo...