LoCo method trains reasoning models to stop thinking sooner

A group of researchers has proposed LoCo, a training method that teaches reasoning models to conclude once they already have enough information instead of continuing to deliberate. The paper, posted on arXiv, identifies candidate overthinking states inside a model's own traces, freezes the reasoning up to that point, and regenerates shorter continuations that are verified. State-conditioned preference pairs from those pairs are then used for direct preference optimization, so the model learns when to keep going and when to stop. All training data came from self-generated mathematical traces. Completions shrank 41.7%–45.4% on five math benchmarks and 23.4%–36.3% on three code benchmarks, with accuracy maintained or improved. The authors say code and model will be released on GitHub.