Liquid AI Open-Sources Antidoom: A Final Token Preference Optimization (FTPO) Method that Reduces Doom Loops in Reasoning Models

Liquid AI has released Antidoom , an open-source method that targets a common failure mode in reasoning models. That failure mode is the doom loop . In a doom loop, a model emits a span. It then repeats that span again and again. The output continues until the context window is exhausted. Small reasoning models are more prone to this, especially on long thinking traces and hard problems.

On an early checkpoint of LFM2.5-2.6B , 10.2% of completions on hard math and coding prompts produced repetitive loops. After Antidoom training, that rate fell to 1.4%. Eval scores improved across the board, attributable entirely to the reduced looping.

Antidoom is a targeted fix, not a broad sampling change. It finds the exact token that begins a loop. It then trains the model to prefer coherent alternatives at that single position. The rest of the distribution stays largely untouched.

The method adapts Antislop . It trains on chosen/rejected pairs that represent a single completion token. The training algorithm is Final Token Preference Optimization (FTPO) , which is similar to DPO.

The training teaches the model nothing new about math or code. It clears the looping that blocked answers the model could already produce.

Liquid AI team attributes doom loops to three mechanisms working together:

Mechanism 1: overtrained tokens plus uncertainty. Some tokens are more likely to be selected in general. Well-known examples in the wild include ‘delve’ and ‘testament.’ Liquid AI team notes this can trace back to synthetic data in the training set. In reasoning traces, high-prior continuations often include discourse markers such as ‘Wait’ or ‘Alternatively.’ These tokens are not inherently bad. They can mark a useful change of strategy, a verification step, or a branch. When the model is uncertain or stuck, they instead become attractive fallback continuations.

For an early LFM2.5-2.6B checkpoint, the most common loop-starting tokens were the following.

Mechanism 2: prior context reinforces the loop. Each repetition pushes every token in the span toward probability that Duan et al. study this in their work on circular reasoning. They link it to a “V-shaped” attention pattern. They find that semantic repetition precedes textual repetition.

Mechanism 3: greedy sampling. Reasoning models usually run at low temperature for stable, reproducible traces. At temperature 0, the most likely token is always selected. A locally reinforced loop then has no exit. Liquid AI reports significant looping even at temp=0.67. Lower temperatures exacerbate the problem.