vanty120/Gpt-5.4-Xhigh-Reasoning-2000x

---
language:
- en
license: apache-2.0
task_categories:
- question-answering
- text-generation
size_categories:
- 1K<n<10K
tags:
- reasoning
- math
- code
- science
- distillation
- chain-of-thought
- gpt-5.4
- gemini-3.1-pro
- thinking
- sft
- hard-reasoning
pretty_name: Gpt-5.4-Xhigh-Reasoning-2750x
---

# Gpt-5.4-Xhigh-Reasoning-2750x

A premium-quality reasoning dataset containing **2,752 elite samples** distilled from **GPT-5.4 XHIGH** (the highest reasoning effort tier of GPT-5.4). Each sample features deep, multi-step Chain-of-Thought traces that are significantly longer and more rigorous than standard GPT-5.4 outputs.

This dataset is specifically designed for **Supervised Fine-Tuning (SFT)** to transform general-purpose language models into powerful reasoning models with explicit thinking capabilities.

## Dataset Summary

| Property | Value |
|---|---|
| **Total Samples** | 2,752 |
| **Teacher Model** | GPT-5.4 XHIGH (Maximum Reasoning Effort) |
| **Seed Data** | [Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) + [Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) + [gemini-3.1-pro-hard-high-reasoning](https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning) |
| **Language** | English |
| **Domains** | Mathematics, Code, Science, STEM, Security, Economics, and 60+ expert-level domains |
| **Avg. Thinking Length** | ~12,600 characters per sample |

### Why XHIGH?

GPT-5.4 supports multiple reasoning effort levels. **XHIGH** is the maximum tier, which forces the model to allocate significantly more compute to its internal chain-of-thought before producing a final answer. This results in:

- **Deeper logical decomposition** compared to standard GPT-5.4 outputs
- **More self-correction steps** within the reasoning trace
- **Higher accuracy** on complex multi-step problems

## Seed Data Sources

### Source 1: Standard Reasoning (2,007 samples)
High-quality prompts sourced from [Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) and [Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered), covering math, code, science, and instruction-following tasks.

### Source 2: Gemini 3.1 Pro Hard Reasoning (745 samples)
Ultra-hard prompts sourced from [Roman1111111/gemini-3.1-pro-hard-high-reasoning](https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning). These prompts were originally generated by an agentic workflow (Gemini 3 Flash as orchestrator) using the following high-intensity system instruction:

> **System Instruction**: Act as a "Super-Intelligence Evaluator". Generate distinct, complex but solvable prompts that require extreme logic.
>
> **Requirements**:
> 1. **Difficulty**: The question must be unsolvable by simple retrieval. It requires multi-step logic, derivation, or synthesis of conflicting information.
> 2. **Concept**: Pick a specific, niche concept within the target domain.
> 3. **Prompt Text**: The user prompt should be detailed (code snippets, math proofs, or philosophical paradoxes).
> 4. **No Fluff**: Go straight to the hard part.

This source spans 60+ expert-level domains including:
- **Physics**: QFT, General Relativity, Condensed Matter, Thermodynamics
- **Math**: Algebraic Topology, Analytic Number Theory, Category Theory
- **Coding/CS**: CUDA/OpenCL HPC, Database Internals, LLVM IR, ZK-Proofs
- **Biology/Med**: CRISPR Off-target Analysis, Protein Folding, Pharmacokinetics
- **Security**: Prompt Injection Defense, Cryptanalysis, Side-channel Attacks
- **Strategic Logic**: Game Theory, Supply Chain Crisis Modeling, Urban Planning
- **Benchmarks**: ARC-AGI, LiveCodeBench v6, TheoremQA, MathVista

## Domain Distribution

| Category | Count | Percentage |
|---|---|---|
| Mathematics | 1,581 | 57.4% |
| Code | 174 | 6.3% |
| Science | 136 | 4.9% |
| Instruction Following | 116 | 4.2% |
| Prompt Injection & Jailbreak Defense | 70 | 2.5% |
| Algebraic Topology | 65 | 2.4% |
| Bioinformatics Algorithms | 43 | 1.6% |
| Computational Chemistry (DFT) | 41 | 1.5% |
| Other Expert Domains (60+) | 526 | 19.1% |

## Difficulty Distribution

| Difficulty | Count | Description |
|---|---|---|
| Medium | 1,986 | Undergraduate level |
| Hard | 188 | Professional / competition level |
| Extreme | 111 | Research frontier |
| Expert | 124 | PhD-level, research-grade problems |
| Advanced+ | 343 | Advanced, Super-Intelligence, Graduate, Olympiad |

## Dataset Structure

Each sample contains the following fields:

```json
{
"category": "Algebraic Topology",
"difficulty": "Extreme",
"instruction": "The original question or problem statement...",
"thinking": "Full chain-of-thought reasoning trace from GPT-5.4 XHIGH...",
"response": "The final, polished answer..."
}
```

| Field | Description |
|---|---|
| `category` | Domain classification (60+ categories) |
| `difficulty` | Difficulty tier: `medium`, `hard`, `extreme`, `expert`, `advanced`, etc. |
| `instruction` | The original problem or question |
| `thinking` | Complete reasoning trace (Chain-of-Thought) from GPT-5.4 XHIGH |
| `response` | Final solution / answer |

## Generation Pipeline

1. **Seed Selection**: High-quality prompts sourced from three complementary datasets covering standard reasoning (math, code, science) and ultra-hard expert-level domains (60+ fields).
2. **Distillation**: Each prompt was processed through **GPT-5.4** with `reasoning_effort=xhigh`, extracting both the internal reasoning trace and the final output.
3. **Quality Control**: Samples with empty thinking or responses were filtered out. Prompt injection artifacts were cleaned from the input.

### Training Format (ChatML with Thinking)
```
<|im_start|>system
You are a helpful assistant that thinks step-by-step.<|im_end|>
<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
<thinking>
{thinking}
</thinking>
{response}<|im_end|>
```

## Disclaimers

- **LLM Hallucinations**: While GPT-5.4 XHIGH produces highly rigorous outputs, a small number of reasoning errors may still exist. Sample inspection before fine-tuning is recommended.
- **Expert Verification**: The hard-reasoning subset contains solutions so technical that they may require subject-matter experts (PhDs) to verify accuracy.
- **License**: This dataset is released under the Apache 2.0 license. Usage must comply with [OpenAI's Terms of Service](https://openai.com/policies/terms-of-use).

## Credits

- **Teacher Model**: [GPT-5.4](https://openai.com/gpt-5) by OpenAI (XHIGH reasoning effort)
- **Seed Datasets**:
- [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) (Alibaba-Superior-Reasoning-Stage2)
- [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered)
- [Roman1111111/gemini-3.1-pro-hard-high-reasoning](https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning)
- **Distillation Pipeline**: Built by [vanty120](https://huggingface.co/vanty120)