INSTRUCTION
自然语言指令Natural-language instruction
明确说出要完成的任务。States the task to perform explicitly.
01 · 会做一道题，不等于会听指令01 · Solving one exercise is not general instruction
following
只练一百次“把句子改写得礼貌”，第一次收到“按日期排序”时，学员会知道该换任务吗？After practicing “rewrite politely” one hundred times, will a learner know what to do when first asked to sort by date?
02组合改写、抽取、分类任务，再测试陌生指令Combine Rewrite, Extraction, and Classification, Then Test an Unseen Task
多样性仪表 + 泛化测试diversity gauge + transfer test
状态 AMetric A
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状态 BMetric B
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状态 CMetric C
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所有概率、得分与输出均为固定种子的教学模拟，不代表真实模型。拖回主变量即可复核结论。All probabilities, scores, and outputs are fixed-seed teaching simulations, not real model output. Move the main variable back to verify the conclusion.
03把刚才的变化拆成 4 个零件Break the Change into Four Parts
Instruction TuningRESPONSE
示范响应Demonstration response
给出符合该指令的期望结果。Provides an expected result that follows the instruction.
DIVERSITY
任务多样性Task diversity
改写、抽取、分类提供不同操作形状。Rewrite, extraction, and classification provide different task shapes.
TRANSFER
陌生指令泛化Unseen-task transfer
用未训练的排序任务检查是否只会背题。An unseen sorting task checks whether the model merely memorized exercises.
04最小机制：主交互能逐项验证Minimal Mechanism: Verify Each Link in the Interaction
MECHANISM
instruction diversity ↑ → toy unseen-task transfer ↑ (not
guaranteed)
本页只做概念级玩具模拟：点亮的任务类型越多，陌生排序测试的指示值越高，但不是现实性能承诺。This is a concept-level toy simulation. More task types raise the
unseen sorting indicator, but it is not a real performance
claim.
组合任务Mix tasks
改写 / 抽取 / 分类rewrite / extract / classify
学习指令形状Learn task forms
从语言映射到操作map language to operations
测试陌生任务Test unseen task
排序未在训练组合中sorting was not trained
05只保留改写任务，再拿陌生排序来测Keep Rewrite Only, Then Test Unseen Sorting
主动制造失败Create a failure边界实验Boundary experiment
任务数量很多不等于任务多样。失败按钮只留同一种改写，再观察模型把排序误认成改写。Many examples do not mean diverse tasks. The failure button keeps only rewriting and shows sorting misread as another rewrite.
尚未执行。先在主交互中观察正常机制。Not run yet. Observe the normal mechanism in the main interaction
first.
✗ 样本越多，任务就越多样。More examples always mean more task diversity.
→ ✓ 同一任务重复再多，任务类型仍然只有一种。Repeating one task does not add another task
type.
✗ 指令微调让模型自动掌握所有陌生任务。Instruction tuning guarantees every unseen task.
→ ✓ 多样性可帮助泛化，不是无限能力保证。Diversity can help transfer; it is not an unlimited capability
guarantee.
✗ 它与普通 SFT 完全无关。Instruction tuning is unrelated to ordinary SFT.
→ ✓ 它通常仍用监督配对，只是特别强调多任务指令—响应。It commonly uses supervised pairs, with emphasis on diverse
instructions and responses.
06 · 一句话带走06 · One line to keep