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Reducing Doom Loops with Final Token Preference Optimization

A new method called Antidoom uses Final Token Preference Optimization (FTPO) to precisely target and eliminate repetitive loops (doom loops) in language models, achieving near-complete elimination on multiple models with improved eval scores.

SourceHacker News AIAuthor: dataminer
Reducing Doom Loops with Final Token Preference Optimization
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Key points and analysis

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Key points

  • Doom loops are a common failure mode in reasoning models where the model repeats a span until the context window is exhausted.
  • Antidoom identifies the first token of a loop and trains the model to prefer coherent alternatives at that position.
  • Doom loop rates dropped from 10.2% to 1.4% on LFM2.5-2.6B and from 22.9% to 1% on Qwen3.5-4B.
  • The method also reveals that low-temperature sampling benefits performance once loops are removed.

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