selective latent iterations

Think-at-Hard: Selective Latent Iterations Boost LLM Reasoning Accuracy by Up to 6.8%

A new research paper proposes Think-at-Hard (TaH), a looped transformer that selectively performs latent iterations only on tokens likely to be incorrect. By skipping iterations on 93% of tokens, TaH outperforms always-iterate models by 3.8-4.4% and single-iteration baselines by up to 6.8%, while requiring negligible extra parameters.