@maximelabonne: Antidoom is now in TRL! Remove your doom loops with this one simple trick.
Summary
Antidoom is a tool that helps small reasoning models avoid repetitive loops in complex tasks, now reaching Technology Readiness Level. It addresses the issue where models get stuck and repeat words during long thinking traces.
View Cached Full Text
Cached at: 08/28/26, 09:56 PM
Antidoom is now in TRL! Remove your doom loops with this one simple trick.
Sergio Paniego (@SergioPaniego): catching up on some bookmarked reads from the summer, reading Antidoom from @liquidai
small reasoning models get stuck more easily when the task involves a long thinking trace and a hard problem. It starts repeating the same word over and over again (“Wait”, “Alternatively”…),
Similar Articles
@maximelabonne: New training technique to reduce doom loops! We applied it to LFM2.5-2.6B (SFT checkpoint) Qwen3.5-4B. By reducing doom…
Liquid AI releases Antidoom, an open-source method to reduce doom loops in reasoning models, applied to LFM2.5-2.6B and Qwen3.5-4B, significantly lowering doom-loop rates and improving eval scores.
Reducing Doom Loops with Final Token Preference Optimization
Liquid AI introduces Antidoom, a method using Final Token Preference Optimization to reduce repetitive doom loops in small reasoning models during inference, cutting loop rates from 10.2% to 1.4% while improving evaluation scores.
@LiorOnAI: An open-source fix for one of the most common reasoning model failure modes. One of the biggest AI trends this year isn…
Liquid AI releases Antidoom, an open-source method that fine-tunes reasoning models to break repetitive token loops (doom loops), reducing failure rates from ~23% to 1% on Qwen3.5-4B without retraining or RL.
@helloiamleonie: Working with the @liquidai team on these engineering blogs is just so much fun! Here's what we've been working on: Reas…
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).
@Raman_bansal_: If you’ve trained an LLM, you’ve may have seen doom loop, in which a LLM endlessly repeats the same token or sentence, …
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.