Liquid AI open-sources Antidoom, a final-token preference optimization method that fixes reasoning model doom loops

Liquid AI 开源 Antidoom：基于最终 Token 偏好优化的推理模型死循环修复方法

Reasoning models can get stuck in doom loops, repeating useless tokens until the context window fills up. Liquid AI open-sourced Antidoom, which uses Final Token Preference Optimization (FTPO) to fix this. The method trains the model on 1,040 preference pairs to learn when to stop at the end of reasoning. On DeepSeek V4 Pro, the doom-loop rate dropped from 3.2% to 0.3% without hurting math or coding scores. The post doesn't disclose training cost or how well it transfers to non-DeepSeek models.

Why it matters: Liquid AI open-sourced a practical fix for reasoning model doom loops, dropping the rate from 3.2% to 0.3% on DeepSeek V4 Pro — solid numbers. Not scoring higher because it's a single blog post with no paper or third-party validation yet; 78 for a strong single-source piece.