How ATLAS Works
ATLAS uses a hybrid architecture that separates complex offline reinforcement learning (to train expert teacher models) from hyper-efficient online optimization (to adapt to specific tasks). This enables any student model to achieve enhanced performance with guaranteed safety. Our system provides a production-ready solution for systematically improving model performance through adaptive teaching protocols, delivering measurable gains like +165% performance improvement in just 2 hours.Key Features & Performance
Hybrid Learning Architecture
Separates heavy offline RL from light online optimization, enabling rapid, production-safe adaptation. This delivered a +165% performance gain in just 2 hours.
Adaptive Teaching Protocol
A two-pass system that diagnoses student capability, then provides targeted guidance. This results in a 97% non-degradation rate and a 50% token reduction.
Compounding Intelligence
Systematically learns from experience to create reusable skills that transfer across domains, resulting in 15x faster incident resolution in our SRE case study.
Universal Compatibility
Enhances any student model (GPT-5, Claude-4, etc.) without modifying weights, delivering a +15.7% average accuracy improvement across models.
Why ATLAS?
| Approach | Limitation | How ATLAS Solves It |
|---|---|---|
| Traditional RL | Requires massive compute; static rewards | Hybrid Architecture: Separates heavy offline training from light, adaptive online optimization. |
| Fine-Tuning | Risks catastrophic forgetting; requires constant retraining | Model-Agnostic Teaching: Enhances any student model without modifying its weights, preserving all original capabilities. |
| Prompt Engineering | Inconsistent, brittle, and hard to scale | Systematic & Reproducible: Evolves teaching strategies based on performance data, creating a durable knowledge base. |
| Retrieval/Memory | Addresses knowledge gaps but not reasoning | Improves Core Logic: The teacher-student paradigm directly enhances the student’s reasoning process. |
Get Started
Quickstart: Pre-trained Models
Deploy ATLAS in minutes using our pre-trained teacher models. No training required.
Custom Training
Build a custom teacher model optimized for your specific domain and requirements.
Research & Resources
Learn more about the methodology and science behind ATLAS:- ATLAS Technical Report (PDF) - Complete methodology, benchmarks, and implementation details
- Arc Research - Our latest research advancing continual learning systems
- GitHub Repository - Source code, examples, and issue tracking
- HuggingFace Models - Pre-trained teacher and student models