Real-Time Progress Prediction in Reasoning Language Models 文章

arXiv:2506.23274v4 Announce Type: replace-cross Abstract: Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic tasks. However, as these models operate over increasingly long time horizons, their internal progress becomes opaque to users, making expectation management and real-time oversight difficult. In this work, we investigate whether real-time progress prediction is feasible for such models. We first test whether hidden states encode progress information by discretizing reasoning trajectories and training a linear probe to classify reasoning states. We then fine-tune models to generate progress estimates from 0--100\% during chain-of-thought reasoning. Our strongest progress-reporting checkpoint reaches 0.161 MAE on mathematical reasoning traces and outperforms position baselines in this setting.