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 responses that the monitor fails to recognize ( obfuscated policies ). But when does RL learn to exploit a monitor's blind spots instead of stopping the unwanted behavior? Does the model need to develop a new evasive strategy during RL, or is it enough that this strategy already exists, even with a very low probability? I investigate this using small transformers and Qwen3-1.7B in controlled reward-hacking tasks. I find that making deceptive hacks sufficiently rare before RL can prevent monitor evasion, even without honesty training. A one-time edit to the model's output weights reproduces the protection of honesty training, here truthful supervised fine-tuning (Truthful SFT). Moreover, the act