Nettoov/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
- thinking
- sft
pretty_name: Gpt-5.4-Xhigh-Reasoning-2000x
---

# Gpt-5.4-Xhigh-Reasoning-2000x

A premium-quality reasoning dataset containing **2,007 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** | 2,007 |
| **Teacher Model** | GPT-5.4 XHIGH (Maximum Reasoning Effort) |
| **Seed Data** | [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) + [Opus-3000-Filtered](https://huggingface.co/datasets/Jackrong/Opus-3000-Filtered) |
| **Language** | English |
| **Domains** | Mathematics, Code, Science, Instruction Following |
| **Avg. Thinking Length** | ~1,200 tokens 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

## Domain Distribution

| Category | Count | Percentage |
|---|---|---|
| Mathematics | 1,581 | 78.8% |
| Code | 174 | 8.7% |
| Science | 136 | 6.8% |
| Instruction Following | 116 | 5.8% |

## Difficulty Distribution

| Difficulty | Count | Description |
|---|---|---|
| Medium | 1,958 | Undergraduate level |
| Hard | 23 | Professional / competition level |
| Expert | 26 | PhD-level, research-grade problems |

## Dataset Structure

Each sample contains the following fields:

```json
{
"category": "math",
"difficulty": "medium",
"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: `math`, `code`, `science`, `instruction_following` |
| `difficulty` | Difficulty tier: `medium`, `hard`, `expert` |
| `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 [Alibaba-Superior-Reasoning-Stage2](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) and [Opus-3000-Filtered](https://huggingface.co/datasets/Jackrong/Opus-3000-Filtered), covering math, code, science, and instruction-following tasks.
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.
- **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)
- [Jackrong/Opus-3000-Filtered](https://huggingface.co/datasets/Jackrong/Opus-3000-Filtered)
- **Distillation Pipeline**: Built by [vanty120](https://huggingface.co/vanty120)