@maximelabonne: New training technique to reduce doom loops! We applied it to LFM2.5-2.6B (SFT checkpoint) Qwen3.5-4B. By reducing doom…
Summary
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.
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New training technique to reduce doom loops!
We applied it to LFM2.5-2.6B (SFT checkpoint) Qwen3.5-4B.
By reducing doom loops, it also improves downstream evals.
We open-source the training code and training dataset on @huggingface
Liquid AI (@liquidai): Today we release Antidoom, an open-source method that removes a common failure mode in reasoning models: the doom loop.
Doom-loop rates before and after, with eval scores up across the board:
> Early LFM2.5-2.6B checkpoint: 10.2% → 1.4%
> Qwen3.5-4B: 22.9% → 1% (greedy
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