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A Substack article explains the 'doom loop' problem in LLMs where models repeat tokens endlessly, and introduces Final Token Preference Optimization (FTPO) from Liquid AI as a method to detect and fix such loops during fine-tuning.
The post explains doom loops in reasoning models where the model repeats tokens like 'Wait' until the context fills up, and introduces FTPO (Final Token Preference Optimization) as a training-time fix. The associated Antidoom tool reduces doom loop rates significantly (e.g., from 22.9% to 1% on Qwen3.5-4B).