Datasets

Available Datasets

ATLAS provides curated datasets for training adaptive teachers and evaluating system performance.

Primary Dataset

Arc-ATLAS-Teach-v0

View on Hugging Face

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:

{
  "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:

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:

{
  "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:

math_data = dataset.filter(lambda x: x['domain'] == 'math')

Code Generation Subset

Focus: Programming tasks and debugging Example:

{
  "prompt": "Write a function to validate email addresses",
  "ground_truth": "def validate_email(email):...",
  "teaching": "Consider regex pattern, edge cases like..."
}

Filtering:

code_data = dataset.filter(lambda x: x['domain'] == 'code')

SRE/Debugging Subset

Focus: System reliability and debugging scenarios Example:

{
  "prompt": "Service returns 503 errors intermittently",
  "ground_truth": "Check service mesh configuration...",
  "teaching": "Systematic approach: 1) Check Istio configs..."
}

Filtering:

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 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:

Data format requirements
Preprocessing JSONL exports from the SDK
Postgres-backed workflows
Quality validation

Contributing Data

We welcome contributions to improve ATLAS datasets:

Format your data according to the schema
Validate quality using provided tools
Test with models to ensure compatibility
Submit PR with data and documentation

See Contributing Guidelines for details.

License and Citation

Datasets are released under Apache 2.0 license. If you use these datasets, please cite:

@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

Training Guide

Train models with datasets

Models

Pre-trained ATLAS models

Adaptive Tool Use

Production example with MCP integration

Technical Report

Dataset methodology

Getting Started

SDK Guides

Examples

Training

Core Concepts

Reference

Benchmarks

Available Datasets

Primary Dataset

Arc-ATLAS-Teach-v0

View on Hugging Face

Domain-Specific Subsets

Mathematics Subset

Code Generation Subset

SRE/Debugging Subset

Data Quality Metrics

Coverage Statistics

Performance Baselines

Creating Custom Datasets

Contributing Data

License and Citation

Next Steps

Training Guide

Models

Adaptive Tool Use

Technical Report

​Available Datasets

​Primary Dataset

​Arc-ATLAS-Teach-v0

View on Hugging Face

​Domain-Specific Subsets

​Mathematics Subset

​Code Generation Subset

​SRE/Debugging Subset

​Data Quality Metrics

​Coverage Statistics

​Performance Baselines

​Creating Custom Datasets

​Contributing Data

​License and Citation

​Next Steps

Training Guide

Models

Adaptive Tool Use

Technical Report

Available Datasets

Primary Dataset

Arc-ATLAS-Teach-v0

Domain-Specific Subsets

Mathematics Subset

Code Generation Subset

SRE/Debugging Subset

Data Quality Metrics

Coverage Statistics

Performance Baselines

Creating Custom Datasets

Contributing Data

License and Citation

Next Steps