How Liquid AI Cut Doom Loops from 10% to 1% With a Single-Token Fix
To overcome this issue, at Liquid AI we use "Antidoom" training using a technique called Final Token Preference Optimization (FTPO).
In this tutorial, you will learn:
• what a doom loop is
• how to reduce doom loops with FTPO
• how to implement a custom DPOTrainer class in TRL for FTPO
Tutorial: https://lnkd.in/gWCNB62P
This checks out precisely against Liquid AI's own published research — the terminology, the technique name, and the framing all match, not just approximately but essentially word for word.
"Doom loops" and "Antidoom" via FTPO — confirmed exactly, and this is Liquid AI's own naming, not a third party's characterization. Their own blog post title is literally "Reducing Doom Loops with Final Token Preference Optimization," and they explicitly call their approach "Antidoom."
The mechanism matches precisely. FTPO identifies the exact token that begins a loop, trains the model to prefer coherent alternatives at that single position, and leaves the rest of the distribution largely untouched — it's described as adapting the Antislop method, training on chosen/rejected pairs representing a single completion token, in the DPO family of preference-optimization algorithms.
"Small thinking models" is accurate framing, not exaggeration. The published results are specifically on small reasoning models: an early LFM2.5-2.6B checkpoint (10.2% of completions on hard math/coding prompts had repetitive loops, falling to 1.4% after Antidoom training) and Qwen3.5-4B (22.9% → 1%). Both are small (2-4B parameter) models, matching your framing exactly — and this is worth adding to your post as concrete evidence, since it's a strong, specific, and verifiable number your post currently doesn't include.
Open-source status and code — confirmed. The code and training dataset (LiquidAI/antidoom-mix-v1.0) are public on GitHub (Liquid4All/antidoom), and the training pipeline reportedly runs in just 2-3 hours for a 2-4B model.
One thing I couldn't independently verify: the specific claim that the tutorial teaches "how to implement a custom DPOTrainer class in TRL for FTPO." This is technically coherent — FTPO is explicitly described as DPO-family, so subclassing Hugging Face's TRL DPOTrainer to implement its specific mechanics (final-token-only training, multiple chosen tokens, logit-space KL-like loss) is a very plausible and sensible practical approach — but I couldn't confirm this specific implementation detail without accessing the tutorial itself, so I'd treat it as likely accurate rather than independently confirmed.
Nothing here is fabricated or inflated — if anything, the post undersells its own numbers by not citing them.