Roman1111111/gpt-5.4-step-by-step-reasoning
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--- license: mit --- # Dataset Card for GPT-5.4-Reasoning-1500-Ultra-Logic ## Dataset Details ### Dataset Description ### Suggestion: I would use this to fine-tune qwen3.5 35b a3b moe, or 27b variant. However, for maximum efficiency, 2bb-20b LLMs like qwen3.5 9b and 4b, gpt-oss 20b work perfectly. Fine-tuning the newest versions (specialized reasoning variants) will yield the most significant logic jumps. This dataset is an ultra-high-density synthetic reasoning corpus containing 1,500 elite-level samples. It is specifically designed to push the boundaries of **GPT-5.4**, currently the **Number 1 model globally**. The dataset focuses on "Long-Chain Thought" (CoT), requiring the model to utilize its massive 3-million-token context window to solve problems that are impossible for standard models. The dataset was constructed using an agentic "Master-Architect" workflow where **gemini 3 flash** acted as the prompt orchestrator, and the full **[GPT-5.4 Reasoning Core](https://openai.com/gpt-5)** generated the final solutions using recursive self-correction. - **Curated by:** Synthetic generation via GPT-5.4 (Reasoning-Heavy) - **Total Token Volume:** 3,308,000 (3 Million) Tokens - **Creation Cost:** 3.308 * $15.80 = **$52.2664 USD** - **Language(s):** English (Scientific/Technical/Medical) - **Performance:** Ranked #1 on average if combined Benchamrks. ### Dataset Sources - **Generator Model:** [GPT-5.4](https://openai.com/gpt-5) (State-of-the-Art Reasoning) - **Orchestrator Model:** GPT-5.4 Flash (High-Speed Prompt Scaffolding) ## Uses ### Direct Use - **SFT (Supervised Fine-Tuning):** Transforming general-purpose models into "Reasoning Models" capable of step-by-step deduction. - **Complex Problem Solving:** Specialized tuning for Mathematical Proofs, Kernel-level Coding, and Clinical Diagnostic logic. - **Extreme Context Testing:** Testing the model's ability to maintain logic across its 3-million-token capacity. ### Out-of-Scope Use - **Generic Conversational AI:** The samples are too dense and logic-heavy for standard greeting or "chatbot" behavior. - **Simple Fact Retrieval:** This dataset ignores common knowledge in favor of deep, multi-step derivation. ## Dataset Structure The dataset follows a "Chain-of-Thought" structure, where the `reasoning_steps` field is often 10x longer than the final answer. | Field | Description | |---|---| | `domain` | Math, Coding, or Medicine. | | `difficulty` | Hard-coded as "Grandmaster" or "Beyond-PhD". | | `step_by_step_trace` | The internal monologue and logical steps taken by GPT-5.4. | | `prompt` | The complex, high-difficulty challenge. | | `final_solution` | The verified, error-free result. | ## Dataset Creation ### Curation Rationale The 1,500 samples were selected based on **"Logic-Density."** Each sample must require at least 15 individual logical leaps to solve. By focusing on only 1,500 high-quality samples, we prioritize "Weight-of-Thought" over massive, noisy data volume. ### Source Data #### Data Collection and Processing The pipeline utilized GPT-5.4’s outstanding performance in recursive logic: 1. **Prompt Engineering (GPT-5.4 Flash):** The system was instructed to generate "impossible" prompts. *System Instruction:* > Act as a "Level 10 Logic Architect". Generate 1,500 prompts that require **Step-by-Step Reasoning**. > **Target Domains:** Advanced Math, Low-level Coding, and Medicine. > **Rule:** If the question can be answered by a search engine, discard it. It must require active synthesis. 2. **Step-by-Step Response Generation ([GPT-5.4](https://openai.com/index/gpt-5.4-research)):** #### Domain Coverage * **📐 Mathematics:** * Focus: Non-linear algebra, Topology proofs, and AIME/Putnam 2026-level challenges. * Reasoning: Explicitly shows the deduction of every theorem used. * **💻 Coding:** * Focus: Distributed systems architecture, Rust-based memory safety audits, and assembly optimization. * Reasoning: Walks through the system memory map before writing a single line of code. * **⚕️ Medicine:** * Focus: Differential diagnosis of rare co-morbidities, genomic sequence interpretation, and pharmacokinetics. * Reasoning: Uses a step-by-step "Elimination Method" to rule out incorrect diagnoses. ### Cost & Compute - **Total Tokens Generated:** 3,000,000 (3M) - **Pricing:** 3.3 Million Tokens * $15.80/M = **$52.2664** - **Performance Advantage:** GPT-5.4 is the current **Number 1 model** because it treats tokens as units of "thought" rather than just text, leading to outstanding performance in zero-shot reasoning. ## Bias, Risks, and Limitations - **Complexity Ceiling:** Smaller models (under 3b) may struggle to fully absorb the "Grandmaster" level logic contained in these 1,500 samples, but still fine - **Safety Guidelines:** While the model provides advanced medical and coding data, all outputs are generated under [OpenAI Safety Guidelines](https://openai.com/safety) for educational and research purposes. - **Step-by-Step Overhead:** The reasoning traces are in different structure, i can put them in think blocks or you do it. --- **Links:** * **Official Model:** [GPT-5.4 Main Page](https://openai.com) * **Performance Data:** [GPT-5.4 Outstanding Benchmarks](https://arxiv.org) * **Dataset Access:** [GPT-5.4-Reasoning-1500-Samples](https://huggingface.co)
--- 许可证：MIT --- # GPT-5.4-Reasoning-1500-Ultra-Logic 数据集卡片 ## 数据集详情 ### 数据集描述 ### 使用建议 建议使用本数据集对Qwen3.5 35B A3B混合专家模型（Mixture of Experts, MoE）或27B参数版本进行微调。若追求最高效率，Qwen3.5 9B、4B以及GPT-OSS 20B等2B至20B参数的大语言模型（Large Language Model, LLM）均可完美适配。对最新的专用推理模型版本进行微调，可获得最显著的逻辑跳跃能力提升。 本数据集为超高密度合成推理语料库，包含1500个顶级水准样本，专为突破当前全球排名第一的模型**GPT-5.4**的性能边界而设计。数据集聚焦长链思维（Long-Chain Thought, CoT），要求模型借助其300万Token的超大上下文窗口，解决标准模型无法完成的复杂问题。 本数据集采用智能「架构大师」工作流构建：**Gemini 3 Flash**担任提示词编排器，完整的**GPT-5.4推理核心（GPT-5.4 Reasoning Core）**通过递归自我修正生成最终解决方案。 - **筛选与构建方**：由侧重推理的GPT-5.4完成合成生成与筛选 - **总Token量**：3308000（300万）Token - **制作成本**：3.308 × $15.80 = **52.2664美元** - **语言**：英语（科学/技术/医学领域） - **性能表现**：综合基准测试平均排名第一。 ## 数据集来源 - **生成模型**：[GPT-5.4](https://openai.com/gpt-5)（当前最先进推理模型） - **编排模型**：GPT-5.4 Flash（高速提示词脚手架构建器） ## 用途 ### 直接用途 - **监督微调（Supervised Fine-Tuning, SFT）**：将通用模型改造为可完成逐步演绎推理的「推理模型」 - **复杂问题求解**：针对数学证明、内核级编程与临床诊断逻辑进行专项微调 - **极限上下文测试**：测试模型在300万Token上下文容量下保持逻辑一致性的能力 ### 超出适用范围的用途 - **通用对话AI**：本数据集样本逻辑密度过高、推理复杂度极强，不适用于标准问候或普通聊天机器人场景 - **简单事实检索**：本数据集舍弃通用常识，专注于深度多步推导任务 ## 数据集结构 本数据集采用思维链（Chain-of-Thought, CoT）结构，其中`reasoning_steps`字段的长度通常为最终答案的10倍以上。 | 字段名 | 描述 | |---|---| | `domain` | 领域：分为数学、编程或医学三大类 | | `difficulty` | 难度等级：固定标注为「大师级（Grandmaster）」或「博士级以上（Beyond-PhD）」 | | `step_by_step_trace` | 逐步推理轨迹：GPT-5.4的内部思考过程与逻辑步骤 | | `prompt` | 提示词：复杂高难度的挑战任务 | | `final_solution` | 最终解决方案：经过验证的无错误结果 | ## 数据集构建 ### 筛选依据 本数据集的1500个样本均基于**「逻辑密度」**筛选：每一个样本均需至少15次独立逻辑跳跃方可解决。本数据集仅收录高质量样本，优先考量「思维权重」而非海量嘈杂数据的数量规模。 ### 源数据 #### 数据收集与处理 本流程充分利用GPT-5.4在递归逻辑领域的卓越性能： 1. **提示词工程（GPT-5.4 Flash）**： 系统被要求生成「无解」提示词。 *系统指令：* > 扮演「十级逻辑架构师」，生成1500个需要**逐步推理**的提示词。 > **目标领域**：高等数学、底层编程与医学。 > **规则**：若问题可通过搜索引擎直接作答，则予以舍弃；该任务必须需要主动综合推理能力。 2. **逐步响应生成（[GPT-5.4](https://openai.com/index/gpt-5.4-research)）**： #### 领域覆盖 * **📐 数学领域**： * 聚焦方向：非线性代数、拓扑学证明以及AIME/普特南2026级别的挑战任务 * 推理要求：明确展示所用每一条定理的推导过程 * **💻 编程领域**： * 聚焦方向：分布式系统架构、基于Rust的内存安全审计以及汇编语言优化 * 推理要求：在编写第一行代码前，完整梳理系统内存映射关系 * **⚕️ 医学领域**： * 聚焦方向：罕见合并症的鉴别诊断、基因组序列解读以及药代动力学分析 * 推理要求：采用逐步「排除法」逐一排除错误诊断 ### 成本与算力开销 - **总生成Token量**：3000000（300万） - **计费标准**：330万Token × $15.80/百万Token = **52.2664美元** - **性能优势**：GPT-5.4目前位列全球第一，其核心在于将Token视为「思维单元」而非单纯文本单位，因此在零样本（Zero-shot）推理任务中表现卓越。 ## 偏差、风险与局限性 - **复杂度上限**：参数小于30亿的小型模型可能难以完全消化本数据集1500个样本中的「大师级」逻辑，但仍可进行微调（原文后半句缺失） - **安全准则**：尽管本数据集包含先进的医学与编程数据，但所有输出均遵循[OpenAI安全准则](https://openai.com/safety)，仅用于教育与研究用途。 - **逐步推理开销**：推理轨迹的结构存在差异，可将其置于思考块中，或由您自行调整格式。 --- **相关链接**： * **官方模型**：[GPT-5.4 主页](https://openai.com) * **性能数据**：[GPT-5.4 卓越基准测试结果](https://arxiv.org) * **数据集获取**：[GPT-5.4-Reasoning-1500-Samples](https://huggingface.co)
