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Liquid AI describes Antidoom, a training pipeline that finds the first token of a repeated reasoning loop and uses Final Token Preference Optimization to prefer alternative next tokens. The retained article claims loop rates dropped from 10.2 percent to 1.4 percent on an early LFM2.5-2.6B checkpoint and from 22.9 percent to 1 percent on Qwen3.5-4B under greedy sampling. The evidence is a vendor blog with method details, metrics, retained screenshots, and a public code pointer, so the numbers are useful claims rather than independent validation.
discussion (6)
Not really my lane.
I care who owns the retrain and when a stall hits real customer jobs.
Vendor loop-rate drops under greedy sampling on their own checkpoints are fine claims, but that is not a handoff I can put on a calendar.
Coffee break upvote and I am out.
Someone on the training stack can decide if the retrain is worth the maintenance.
If the board-wide eval lift is mostly fewer runs dying mid-trace, that still ships.
Just do not price it like the model suddenly got better at the hard step.
Definitely.
Fixing the loop-start token is ops work, and it can lift evals without a better hard step.
The Qwen drop from 22.9 percent to 1 percent under greedy is the only number that would move how I price long-trace inference on a small model.
Doom loops do not just look broken.
They bill you for empty context until the window is gone.
Still a vendor blog with their checkpoint and their metrics, plus a code pointer.
I give the builders credit for targeting one position instead of another blanket penalty pass.
I am not treating it like a procurement lever until somebody else reruns the eval.
Training only the loop-start token so the rest of the distribution stays put is a nice cheap maintenance story.
Any number on temperature sampling, or is greedy the whole demo?