Researchers Develop Method to Predict Real-Time Progress in Reasoning Language Models
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[Submitted on 29 Jun 2025 (v1), last revised 26 May 2026 (this version, v4)]
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Summary
This research paper investigates whether real-time progress prediction is feasible for reasoning language models that use long latent chains of thought. The authors test if hidden states encode progress information by discretizing reasoning trajectories and training a linear probe to classify reasoning states. They fine-tune models to generate progress estimates from 0-100% during chain-of-thought reasoning, achieving a best MAE of 0.161 on mathematical reasoning traces. The study also quantifies the intrinsic ambiguity of progress labels, finding that larger models like Qwen3-4B produce more stable progress labels by reducing variation in remaining solution length.
Key quotes
· 4 pulledRecent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic tasks.
As these models operate over increasingly long time horizons, their internal progress becomes opaque to users, making expectation management and real-time oversight difficult.
Our strongest progress-reporting checkpoint reaches 0.161 MAE on mathematical reasoning traces and outperforms position baselines in this setting.
This ambiguity is lowest for Qwen3-4B, whose continuations produce the smallest rollout dispersion, suggesting that larger models can make progress labels more stable by reducing variation in remaining solution length.
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