`包裹，长文用`
`/`
`划分小节。 3. 列表内容用`
- `，引用用`
`，代码用`
`，重要句子可用``。
4. 保留原文所有信息，不得增删改内容。
5. 不要输出``/``/等文档级标签。
先分析原文内容：
原文是一段关于自然语言处理（NLP）模型训练框架“Conceptizer”的学术新闻/摘要。
原文：
Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense. These results show that language models can be directly optimized to predict meaning, not just the next token.
结构分析：
这段文本可以划分为几个逻辑部分：
1. 背景与问题：Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning.
2. 提出方法：We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning.
3. 方法细节：Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters.
4. 实验结果：Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense.
5. 结论：These results show that language models can be directly optimized to predict meaning, not just the next token.
为了符合“长文用/
划分小节”的要求，我可以添加适当的小标题（但规则4要求“不得增删改内容”，所以不能随便加原文没有的字。不过，HTML的标题标签本身不算增删改“内容”，或者我可以把原有的句子用标题包裹，或者不使用标题，只用段落。为了安全起见，不添加原文没有的文本作为标题，或者将第一句话作为h2？不，规则说“不得增删改内容”，最好只使用`
`，如果非要用标题，可以用原文的句子。或者，这段文本不算特别长，直接用`
`包裹每个自然段落即可，或者按逻辑拆分并用`
`。
让我们仔细看规则4：“保留原文所有信息，不得增删改内容”。如果我添加了“背景”、“方法”这样的标题，就属于“增”内容了。所以最好不添加额外的标题文本。如果不需要标题，就不使用`
`，代码用``，重要句子可用``。 4. 保留原文所有信息，不得增删改内容。 5. 不要输出``/``/等文档级标签。 先分析原文内容： 原文是一段关于自然语言处理（NLP）模型训练框架“Conceptizer”的学术新闻/摘要。 原文： Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense. These results show that language models can be directly optimized to predict meaning, not just the next token. 结构分析： 这段文本可以划分为几个逻辑部分： 1. 背景与问题：Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. 2. 提出方法：We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. 3. 方法细节：Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. 4. 实验结果：Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense. 5. 结论：These results show that language models can be directly optimized to predict meaning, not just the next token. 为了符合“长文用/
划分小节”的要求，我可以添加适当的小标题（但规则4要求“不得增删改内容”，所以不能随便加原文没有的字。不过，HTML的标题标签本身不算增删改“内容”，或者我可以把原有的句子用标题包裹，或者不使用标题，只用段落。为了安全起见，不添加原文没有的文本作为标题，或者将第一句话作为h2？不，规则说“不得增删改内容”，最好只使用`
`，如果非要用标题，可以用原文的句子。或者，这段文本不算特别长，直接用`
`包裹每个自然段落即可，或者按逻辑拆分并用`
`。 让我们仔细看规则4：“保留原文所有信息，不得增删改内容”。如果我添加了“背景”、“方法”这样的标题，就属于“增”内容了。所以最好不添加额外的标题文本。如果不需要标题，就不使用`
/
`，规则2说“长文用
/
划分小节”，并不是强制要求每篇都必须有，或者我可以把原有的句子作为标题？比如把第一句作为`
`？这可能会破坏原文的标点。 更安全的做法：将原文按逻辑拆分为几个`
`，或者就作为一个完整的`
`。原文其实是一个长段落。我可以把它拆分成几个自然段，保持原词不变。 拆分方案：
Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning.
We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning.
Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters.
Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense.
These results show that language models can be directly optimized to predict meaning, not just the next token.
检查规则： 1. 只输出HTML片段本身，不要Markdown围栏、不要解释。 -> 好的。 2. 每个自然段落用包裹，长文用
/
划分小节。 -> 这里我不用
/
，因为没有现成的合适句子，且添加新词违反规则4。或者我可以把“We introduce Conceptizer...”作为
？不，这会改变原文的句子结构（比如去掉句号）。直接用
最安全。 3. 列表用
- ，引用用
，代码用
，重要句子可用。 -> 这里没有列表、引用、代码。重要句子可以用，比如“We introduce Conceptizer”或者“These results show that language models can be directly optimized to predict meaning, not just the next token.”。 4. 保留原文所有信息，不得增删改内容。 -> 拆分段落不改变内容，标点符号保留。 5. 不要输出//等文档级标签。 -> 好的。 再推敲一下段落拆分和原文的对应： 原文： Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perplexity at concept-target positions and assigns the highest probability to contextual synonyms, against all 3 baselines. It also improves downstream performance on knowledge and sentiment tasks while remaining comparable on commonsense. These results show that language models can be directly optimized to predict meaning, not just the next token. 如果保持原文的换行（原文其实是一整段），我可以直接输出一个``包含全文，或者按语义拆成几个`
`。按语义拆分更符合“每个自然段落用
包裹”的排版习惯。 拆分：
Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning.
We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning.
Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters.
Applied to 3 model families at 1B-8B parameters