这篇论文戳破了推理模型训练中“数据越多越好”的迷思，做模型训练或智能体开发的团队值得一读——它告诉你该关注什么数据，而不是盲目堆量。
一篇关于推理模型训练后如何改进的入门论文指出，更好的推理模型更依赖于可检查的训练证据，而非原始数据规模。论文强调，推理数据不是简单的问答对，真正有价值的是反馈信号，它解释了答案、步骤、工具调用或完整尝试的好坏。作者将推理数据按检查方式分类，包括基于规则的精确检查（数学、代码）、环境检查（智能体工具使用）以及人工或模型判断。论文还揭示了常见误区：长推理链可能是虚假的，更难的数据对某些模型无用，更大的数据集可能仍缺乏关键覆盖。关键结论是，智能体数据应保留失败、重试、恢复等混乱信息，因为学习信号往往隐藏其中。
A Primer paper about how reasoning models improve …
A Primer paper about how reasoning models improve after training
Shows that better reasoning models depend less on raw data size and more on checkable training evidence.
reasoning data is NOT simple question-and-answer pairs. The useful part is often the feedback that says why an answer, step, tool action, or full attempt was good or bad.
A prompt and a response tell you what a model said, but not why that answer became learnable, which judge blessed it, which failures were hidden, or whether the skill was already inside the base model.
The core idea is to describe each training example as a record that includes the task, the model’s behavior, the checking signal, and metadata about where it came from.
The authors sort reasoning data by how it can be checked, such as exact rule-based checks for math and code, environment checks for agents using tools, and human or model judgments when no exact checker exists.
They also explain why common assumptions fail, because long reasoning traces may be fake, harder examples may be useless for some models, and larger datasets may still miss important coverage.
The key point is that agent data should preserve mess: failed actions, retries, recoveries, state differences, and terminal checks, because that is where learning signal often lives.
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Link – arxiv. org/abs/2606.02113
Title: "A Primer in Post-Training Reasoning Data: What They Know About How It Works"