Semantic Step Prediction Boosts Multi‑Step Reasoning Efficiency in LLMs
The paper introduces Semantic Step Prediction, a technique that samples hidden‑state boundaries at reasoning step transitions to regularize LLM trajectories toward smooth, locally linear paths. This yields up to 168× more accurate multi‑step latent forecasts than baseline methods, promising data‑efficient inference for reasoning models.