Liquid AI Open-Sources Antidoom: A Final Token Preference Optimization (FTPO) Method that Reduces Doom Loops in Reasoning Models
Doom loops plaguing reasoning models just got solved. Liquid AI's open-source method cuts failure rates from 22.9% to 1%.
Why it matters
Liquid AI releases a targeted solution to a critical failure mode in reasoning models—doom loops that waste context and degrade output quality. This is a meaningful technical advancement in model reliability that competitors will need to implement or match.
The key facts
6 to knowLiquid AI open-sourced Antidoom method targeting doom loops in reasoning models
Final Token Preference Optimization (FTPO) retrains only the token position causing repetition
LFM2.5-2.6B: doom-loop rates reduced from 10.2% to 1.4%
Qwen3.5-4B: doom-loop rates reduced from 22.9% to 1%
Generation, detection, and FTPO trainer components released as open source
Addresses failure mode where models repeat token spans until context window exhaustion
Go to the source
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Publisher excerpt: Liquid AI released Antidoom, an open-source method that targets doom loops in reasoning models. A doom loop repeats a span until the context window is exhausted. Antidoom finds the token that starts the loop and retrains only that position using Final Token Preference Optimization (FTPO). On…