Antidoom provides open-source recipe for reducing reasoning loops
The Neuron 1 week ago
Antidoom is an open-source tool that reduces repetition loops in language models by generating preference training data and applying targeted LoRA adapter training via Final Token Preference Optimization. The method identifies where repeated sequences begin, marks the first loop-starting token as rejected, samples alternative tokens, and trains with regularization to prevent overrepresentation of specific tokens. Users can apply Antidoom to their models by cloning the repository, configuring a base checkpoint, generating 15,000–20,000 preference pairs from prompts, and training with a learning rate around 0.00001–0.00002 until the chosen token wins on roughly 15–40% of samples.
