#progress-prediction News & Analysis

Real-Time Progress Prediction in Reasoning Language Models

Researchers have developed methods to predict real-time progress in reasoning language models with long chains of thought, achieving a 0.161 MAE on mathematical tasks. The work addresses the opacity problem in extended reasoning by training linear probes on hidden states and fine-tuning models to generate percentage-based progress estimates, while quantifying the inherent ambiguity in progress labeling across different model sizes.