Rohan Paul@rohanpaul_ai · X论文指出，更好的推理模型更依赖可验证的训练证据，而非原始数据规模。推理数据的关键不是简单问答对，而是提供答案、步骤、工具操作或完整尝试好坏判断的反馈信号。每个训练样本应描述为包含任务、模型行为、检查信号和元数据的记录。研究者按检查方式分类：数学和代码用精确规则、智能体工具用环境检查，无精确检查器时用人类或模型判断。常见误区包括：长推理链可能虚假、更难样例对部分模型无效、更大数据集仍可能缺失关键覆盖。智能体数据应保留失败动作、重试、恢复、状态差异和终端检查等“混乱”信息，因为学习信号常在其中。
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"