Real-Time Progress Prediction in Reasoning Language Models

• URL: http://arxiv.org/abs/2506.23274v3
• Date: Wed, 08 Oct 2025 12:11:48 GMT
• Title: Real-Time Progress Prediction in Reasoning Language Models
• Authors: Hans Peter Lynsgøe Raaschou-jensen, Constanza Fierro, Anders Søgaard,
• Abstract summary: In this work, we investigate whether real-time progress prediction is feasible.<n>We discretize progress and train a linear probe to classify reasoning states.<n>We then introduce a two-stage fine-tuning approach that enables reasoning models to generate progress estimates.
• Score: 41.08450684104994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in reasoning language models -- particularly those that use long, latent chains of thought -- have demonstrated remarkable capabilities in complex, agentic tasks. However, as these models operate over increasingly extended time horizons, their internal progress becomes opaque to users, complicating expectation management and real-time oversight. In this work, we investigate whether real-time progress prediction is feasible. We discretize progress and train a linear probe to classify reasoning states. We then introduce a two-stage fine-tuning approach that enables reasoning models to generate progress estimates (0$\rightarrow$100\%) during inference. Our best fine-tuned model achieves an average error of 10\% for sequences less than 16,000 tokens, offering a practical mechanism for monitoring and interpreting model reasoning in real time.

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