claude-opus-4.6-10000x

This is a high-fidelity reasoning dataset synthesized using Claude Opus 4.6. The dataset is designed to capture the model's internal "Chain of Thought" and reasoning traces, specifically focusing on mathematical accuracy and structured logical deduction.
The dataset is intended for Supervised Fine-Tuning (SFT) and Distillation, allowing smaller open-source models to inherit the sophisticated reasoning patterns of Claude Opus 4.6.
Dataset Description
This collection combines high-difficulty math problems (GSM8K, MATH) with general-purpose logic puzzles and multi-step instructions. Each row includes a hidden reasoning trace where the model "thinks" through the problem before providing the final answer.
By exposing the fine-tuned model to these internal monologues, the resulting model learns process-oriented thinking rather than just pattern-matching for answers.
Why Simple Logic & Math Improves Reasoning
Fine-tuning on "Simple Logic and Math" serves as a cognitive foundation for LLMs for several reasons:
Rule Adherence: Math requires strict following of operations. Training on these paths reduces "hallucinations" in non-math tasks.
Step-by-Step Verification: These examples force the model to break down complex problems into smaller, verifiable units.
Cross-Domain Generalization: The ability to solve a "simple" logic puzzle translates into better coding, legal analysis, and structured writing, as all these tasks rely on the same underlying cognitive architecture of premise → deduction → conclusion.
Stats
## Teacher Model: [Claude Opus 4.6](https://www.anthropic.com/news/claude-opus-4-6)
**Total Cost: $ 87.20 (USD)**

**Total Tokens (Input + Output): 27.2 M**

**Format: JSONL (Conversational with Reasoning Traces)**

**Primary Categories: Mathematics, Symbolic Logic, General Purpose Problem Solving**

### Usage
This dataset is optimized for fine-tuning models such as Qwen3.5 27b,25b a3b, 9b, 4b, 2b, 0.8b to increase their performance on benchmarks like BigBench Hard and GSM8K without increasing their parameter count.

本数据集为使用Claude Opus 4.6生成的高保真推理数据集，旨在捕获模型内部的「思维链（Chain of Thought）」与推理轨迹，重点关注数学准确性与结构化逻辑推演。
本数据集专为监督微调（Supervised Fine-Tuning, SFT）与知识蒸馏设计，可使小型开源模型继承Claude Opus 4.6的复杂推理模式。

数据集说明
本合集整合了高难度数学题（涵盖GSM8K、MATH数据集）、通用逻辑谜题与多步骤指令。每条数据均包含一条隐藏推理轨迹，即模型在给出最终答案前的思考过程。
通过让待微调模型学习这类内部独白，最终模型将习得面向过程的思维方式，而非仅依赖模式匹配生成答案。

为何基础逻辑与数学能提升推理能力
对「基础逻辑与数学」进行微调可为大语言模型（Large Language Model, LLM）构建认知基础，原因如下：
规则依从性：数学解题要求严格遵循运算规则，在这类任务上进行训练可降低非数学任务中的幻觉（hallucination）问题。
分步验证：这类示例会迫使模型将复杂问题拆解为可验证的小型单元。
跨领域泛化：解决「基础」逻辑谜题的能力可迁移至更好的代码编写、法律分析与结构化写作等任务，因为所有这类任务均依赖「前提→推演→结论」这一通用认知架构。

统计信息
## 教师模型：[Claude Opus 4.6](https://www.anthropic.com/news/claude-opus-4-6)
**总成本：87.20美元（USD）**
**总Token数（输入+输出）：27.2 M**
**数据格式：JSONL（包含推理轨迹的对话式数据）**
**核心类别：数学、符号逻辑、通用问题求解**

使用场景
本数据集针对Qwen3.5 27B、25B、A3B、9B、4B、2B、0.8B等模型的微调进行了优化，可在不增加参数量的前提下，提升模型在BigBench Hard、GSM8K等基准测试中的表现。