[billyholevas]

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.

[nullc]

I've got a shim that will ask the model to write a summary of what didn't work, then rolls it rolls back the context to before those attempts with only the summary as advice. Some care has to be taken with cache management that there is an available checkpoint back there.

Appears to help, but I suspect it would be much better with first class support and reinforcement learning to make good use of this kind of advice.

Still certainly needs some tuning because I've noticed the model taking the advice as the word of god and avoiding trying anything remotely like the things that didn't work.