> ## 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.

# Introduction

> A Continual Learning Framework for Production LLM Agents

<div align="center">
  <img src="https://mintcdn.com/arc-469ffbc2/V_wQNyAhKTuyo9N2/images/ATLAS.png?fit=max&auto=format&n=V_wQNyAhKTuyo9N2&q=85&s=04b18ec2d821fe0db136b2f0085016f3" alt="ATLAS Hero Image" width="1200" height="627" data-path="images/ATLAS.png" />
</div>

<Tip>
  **Have questions?** Chat with the docs using the assistant at the bottom.
</Tip>

<br />

## What is ATLAS?

ATLAS is a continual learning framework for production LLM agents. It combines runtime quality control with offline reinforcement learning to improve agent reliability, reduce token costs, and build domain expertise through persistent memory.

The system layers a dual-agent reasoning loop (student + verifying teacher) on top of any model. The [Atlas SDK](https://github.com/Arc-Computer/atlas-sdk) streams causality traces into Postgres, and Atlas Core (this repository) trains new teacher checkpoints via on-policy distillation (GKD) or reinforcement learning (GRPO).

<div align="center">
  <img src="https://mintcdn.com/arc-469ffbc2/eFOCTpd4B9R3jMkV/images/system-architecture.png?fit=max&auto=format&n=eFOCTpd4B9R3jMkV&q=85&s=1896b710891b61e62ee4124511950483" alt="ATLAS System Architecture" width="800" data-path="images/system-architecture.png" />

  <p><em>Atlas runtime captures traces; Atlas Core trains improved models from those traces.</em></p>
</div>

## Which Repository Do You Need?

<CardGroup cols="2">
  <Card title="Atlas SDK" icon="rocket" color="#2563EB">
    **Use if you want to:**

    * Run agents with quality control
    * Get runtime supervision and retries
    * Export traces for later training

    **Repository:** [atlas-sdk](https://github.com/Arc-Computer/atlas-sdk)

    **Installation:** `pip install arc-atlas`

    **Start:** [SDK Quickstart](/sdk/quickstart)
  </Card>

  <Card title="Atlas Core" icon="brain" color="#1E40AF">
    **Use if you want to:**

    * Train custom teacher models
    * Run GKD or GRPO training
    * Fine-tune on exported traces

    **Repository:** [ATLAS](https://github.com/Arc-Computer/ATLAS)

    **Installation:** See [Installation Guide](/installation)

    **Start:** [GKD Training](/training/offline/gkd-training)
  </Card>
</CardGroup>

<Warning>
  **Most teams only need the SDK.** Atlas Core requires GPUs and is only necessary if you are training custom models. The SDK works with any OpenAI-compatible API.
</Warning>

## Why Use ATLAS?

| Benefit                | How                                                                | Result                          |
| ---------------------- | ------------------------------------------------------------------ | ------------------------------- |
| Lower costs            | Adaptive supervision allocates reasoning only when needed          | \~50% token reduction           |
| Higher reliability     | Real-time teacher review catches errors before production impact   | +15.7% avg task success         |
| Continuous improvement | Offline training (GRPO/GKD) updates teacher from production traces | Compounding expertise over time |

→ Full methodology in [Technical Report](/ATLAS-Technical-Report.pdf)
→ System concepts in [Adaptive Dual-Agent Reasoning](/concepts/adaptive-dual-agent-reasoning)

<Note>
  **Runtime vs. Training**: The [Atlas SDK](https://github.com/Arc-Computer/atlas-sdk) handles runtime orchestration and trace export. Atlas Core (this repository) handles offline training (GKD/GRPO).
</Note>

## End-to-End Workflow

| Stage                   | Run This                                                                          | Output                               | Time        |
| ----------------------- | --------------------------------------------------------------------------------- | ------------------------------------ | ----------- |
| Runtime quality control | [`atlas run`](/sdk/quickstart)                                                    | Reviewed traces with reward scores   | Minutes     |
| Export traces           | [`arc-atlas export`](/sdk/export-traces)                                          | JSONL dataset from approved sessions | Minutes     |
| Train teacher (GKD)     | [`atlas-core offline-pipeline`](/training/offline/gkd-training)                   | Distilled teacher checkpoint         | 4-8 hours   |
| Train teacher (GRPO)    | [`atlas-core train recipe@_global_=teacher_rcl`](/training/offline/grpo-training) | RL-optimized teacher checkpoint      | 24-48 hours |

Every stage feeds the next—runtime traces become training data; trained checkpoints redeploy to runtime.

## Getting Started: Two Paths

| I want to...                                   | Path       | Start Here                                       |
| ---------------------------------------------- | ---------- | ------------------------------------------------ |
| Run tasks with dual-agent orchestration        | Atlas SDK  | [SDK Quickstart](/sdk/quickstart)                |
| Wrap my existing agent in quality-control loop | Atlas SDK  | [BYOA Adapters](/sdk/adapters)                   |
| Distill traces into smaller teacher            | Atlas Core | [GKD Training](/training/offline/gkd-training)   |
| Train from rewards (RL)                        | Atlas Core | [GRPO Training](/training/offline/grpo-training) |

<CardGroup cols="2">
  <Card title="SDK Runtime Orchestration" icon="workflow" href="/sdk/quickstart">
    Run your agent with closed-loop learning. Get started in minutes.
  </Card>

  <Card title="Offline Training (Atlas Core)" icon="graduation-cap" href="/training/offline/grpo-training">
    Convert runtime traces into GRPO/GKD training jobs and ship updated teachers.
  </Card>
</CardGroup>

## Research & Resources

* [ATLAS Technical Report (PDF)](/ATLAS-Technical-Report.pdf) - Methodology, benchmarks, implementation details
* [Arc Research](https://www.arc.computer/research) - Latest research on continual learning systems
* [GitHub Repository](https://github.com/Arc-Computer/ATLAS) - Source code and issue tracking
* [HuggingFace Models](https://huggingface.co/Arc-Intelligence) - Pre-trained teachers
* [Evaluation Harnesses](/benchmarks/evaluation-harnesses) - Runtime, reward, and learning measurement scripts