Liquid AI Open-Sources Antidoom to Fix Reasoning Model Doom Loops
Reasoning models often fall into a failure mode known as the "doom loop" during long reasoning chains. Liquid AI recently released an open-source training method called Antidoom, advocating for fixing this issue directly during training rather than applying inference-time patches, which has drawn significant community attention.
Phenomenon and Causes
According to the Liquid AI engineering blog, a doom loop occurs when a model gets stuck mid-reasoning, repeatedly generating tokens like "Wait" or "Let me reconsider" until the context window is completely exhausted. Causes include over-trained tokens dominating during uncertainty and self-reinforcing degradation. Research indicates that small language models with less than 10B parameters are more susceptible to this repetitive output behavior during long reasoning tasks compared to larger models.
Antidoom Solution and Results
To address this pain point, Liquid AI introduced the open-source method Antidoom. Posters nathanrchn and Maxime Labonne noted that the core approach is to fix the model's circular degradation during the training phase, avoiding temporary inference-time patches. After applying this technique, the doom-loop rate for the LFM2.5-2.6B early checkpoint dropped significantly from 10.2% to about 1%, alongside improvements in benchmark scores. The method is also considered broadly applicable for enhancing the reasoning reliability of similar models like Qwen3.5-4B.
2026-07-07 ~ 2026-07-08 · 8 related posts
Primary sources
- Liquid AI Open-Sources Antidoom to Eliminate Inference Doom Loops — soteko ·
- Causes and Fixes for Reasoning Model 'Doom Loops' — helloiamleonie ·
- Fix Thinking Model's Loop Degradation at Training Time — JosephJacks_ ·
- Liquid AI Open-Sources Antidoom to Break Reasoning Model Infinite Loops — helloiamleonie · 2026-07-07
- [source] Causes and Fixes for Reasoning Model 'Doom Loops' — helloiamleonie · 2026-07-07
- Study: Small Models Enter "Doom Loops" During Long Reasoning — bronzeagepapi · 2026-07-08
- Doom Loop: Models Repeat Output Until Context Exhausts — JosephJacks_ · 2026-07-08
- New Training Technique Reduces Model Inference Doom Loops — JosephJacks_ · 2026-07-08
- [source] Fix Thinking Model's Loop Degradation at Training Time — JosephJacks_ · 2026-07-08
- Small Models More Prone to Degradation Loops — JosephJacks_ · 2026-07-08
1 near-duplicate retellings: soteko