Interesting approach. From the practitioner side, the doom loop pattern I hit most with coding agents is context pollution: after two or three failed fix attempts, the failed approaches stay in context and the model keeps circling them. Clearing the session and rewriting the prompt with the exact symptom almost always beats a fourth correction. Curious whether penalizing at the token preference level actually reduces that, or whether it mostly shifts where the loop shows up, since the root cause often seems to be the conversation history rather than the model's token-level tendencies.