> ## Documentation Index
> Fetch the complete documentation index at: https://docs.arc.computer/llms.txt
> Use this file to discover all available pages before exploring further.
# Datasets
> Official ATLAS training and evaluation datasets
## Available Datasets
ATLAS provides curated datasets for training adaptive teachers and evaluating system performance.
## Primary Dataset
### Arc-ATLAS-Teach-v0
Comprehensive teaching interaction dataset for RL training
**Purpose:** Train teacher models to provide adaptive guidance across diverse tasks
**Statistics:**
* **Total examples**: 100,000+ teaching interactions
* **Task domains**: Mathematics, reasoning, coding, debugging
* **Formats**: SFT and RL training splits
* **Languages**: English
**Data Schema:**
```json theme={null}
{
"prompt": "The problem or task requiring solution",
"ground_truth": "Correct answer or solution",
"student_response": "Initial student attempt",
"teaching": "Adaptive guidance provided",
"enhanced_response": "Student response after teaching",
"baseline_score": 0.3,
"with_teaching_score": 0.9,
"reward": 0.6,
"problem_id": "unique_identifier",
"student_level": "weak|moderate|strong",
"domain": "math|reasoning|code|debug"
}
```
**Loading the Dataset:**
```python theme={null}
from datasets import load_dataset
# Load for supervised fine-tuning
sft_data = load_dataset(
"Arc-Intelligence/Arc-ATLAS-Teach-v0",
"sft",
split="train"
)
# Load for reinforcement learning
rl_data = load_dataset(
"Arc-Intelligence/Arc-ATLAS-Teach-v0",
"rl",
split="train"
)
# Load validation set
val_data = load_dataset(
"Arc-Intelligence/Arc-ATLAS-Teach-v0",
"rl",
split="validation"
)
```
**File Structure:**
```
Arc-ATLAS-Teach-v0/
├── training/
│ ├── sft.jsonl # Supervised fine-tuning data
│ └── rl.jsonl # Reinforcement learning data
└── validation/
└── rl.jsonl # Held-out validation
```
## Domain-Specific Subsets
### Mathematics Subset
**Focus:** Step-by-step mathematical reasoning
**Example:**
```json theme={null}
{
"prompt": "Sarah has 24 apples. She gives 1/3 to her brother...",
"ground_truth": "12",
"teaching": "Break down: 1) Calculate 1/3 of 24 = 8..."
}
```
**Filtering:**
```python theme={null}
math_data = dataset.filter(lambda x: x['domain'] == 'math')
```
### Code Generation Subset
**Focus:** Programming tasks and debugging
**Example:**
```json theme={null}
{
"prompt": "Write a function to validate email addresses",
"ground_truth": "def validate_email(email):...",
"teaching": "Consider regex pattern, edge cases like..."
}
```
**Filtering:**
```python theme={null}
code_data = dataset.filter(lambda x: x['domain'] == 'code')
```
### SRE/Debugging Subset
**Focus:** System reliability and debugging scenarios
**Example:**
```json theme={null}
{
"prompt": "Service returns 503 errors intermittently",
"ground_truth": "Check service mesh configuration...",
"teaching": "Systematic approach: 1) Check Istio configs..."
}
```
**Filtering:**
```python theme={null}
sre_data = dataset.filter(lambda x: x['domain'] == 'debug')
```
## Data Quality Metrics
### Coverage Statistics
| Domain | Examples | Avg Length | Unique Patterns |
| --------------- | -------- | ---------- | --------------- |
| Mathematics | 35,000 | 250 tokens | 500+ |
| Code Generation | 30,000 | 400 tokens | 800+ |
| Reasoning | 25,000 | 300 tokens | 600+ |
| Debugging | 10,000 | 350 tokens | 400+ |
### Performance Baselines
| Metric | Baseline | w/ Dual-Agent Loop | Improvement |
| ---------------- | -------- | ------------------ | ----------- |
| Accuracy | 62.3% | 78.0% | +15.7% |
| Completion | 69% | 100% | +31% |
| Token Efficiency | 100% | 50% | -50% |
These figures reflect the closed-loop runtime plus GRPO baseline. Online continual learning now lives in the [`atlas-sdk`](https://github.com/Arc-Computer/atlas-sdk) runtime if you need task-specific adaptation between offline training runs.
## Creating Custom Datasets
To create custom datasets from runtime traces or other sources, see [Custom Dataset Creation](/training/custom-datasets):
* Data format requirements
* Preprocessing JSONL exports from the SDK
* Postgres-backed workflows
* Quality validation
## Contributing Data
We welcome contributions to improve ATLAS datasets:
1. **Format your data** according to the schema
2. **Validate quality** using provided tools
3. **Test with models** to ensure compatibility
4. **Submit PR** with data and documentation
See [Contributing Guidelines](https://github.com/Arc-Computer/ATLAS/blob/main/CONTRIBUTING.md) for details.
## License and Citation
Datasets are released under Apache 2.0 license.
If you use these datasets, please cite:
```bibtex theme={null}
@dataset{atlas_teach_v0,
title={Arc-ATLAS-Teach-v0: Adaptive Teaching Dataset},
author={Arc Intelligence Team},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/datasets/Arc-Intelligence/Arc-ATLAS-Teach-v0}
}
```
## Next Steps
Train models with datasets
Pre-trained ATLAS models
Production example with MCP integration
Dataset methodology