让大模型实时报告解题进度，提升可解释性与可控性。
Real-Time Progress Prediction in Reasoning Language Models
- 用线性探测器分析隐藏状态是否含进度信息
- 最优模型在数学推理中达0.161的平均绝对误差
- 大模型如Qwen3-4B的进度标签更稳定
近期采用长思维链的推理语言模型在复杂代理任务中表现优异。然而，随着推理时间跨度拉长，模型内部进展对用户变得不透明，影响预期管理与实时监控。本文探究此类模型实时进度预测的可行性：首先通过离散化推理轨迹，训练线性探测器分类推理状态以检验隐藏状态是否编码进度信息；随后微调模型，在思维链推理过程中生成0–100%的进度估计。最强的进度报告检查点在数学推理轨迹上达到0.161 MAE，优于位置基准。最后，通过测量同一部分推演路径的进度值波动，量化了进度标签的内在模糊性。结果表明，Qwen3-4B的延续产生最小的推演分散，说明更大模型能通过减少剩余解题长度的变异性，使进度标签更稳定。
原文摘要 · Abstract (English)
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. Finally, we quantify the intrinsic ambiguity of progress labels by measuring how much the implied progress value varies from the same partial rollout. 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。