> ## 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.

# Hybrid Learning Architecture

> Understanding ATLAS's dual-phase approach to model enhancement

## What is Hybrid Learning?

ATLAS separates learning into two phases: **offline foundation training** (one-time, compute-intensive) and **runtime continual learning** (continuous, lightweight). This architecture solves the enterprise AI constraint: lack of high-quality preference data for complex business tasks.

**Key insight:** Train a teacher once on math reasoning (where data is abundant and logic is clear), then apply that reasoning to any domain (CRM, telecom, debugging) without domain-specific retraining.

## The Two-Phase Paradigm

### Phase 1: Offline Foundation Training (Atlas Core)

Establish deep, generalizable skills through reinforcement learning:

```
Offline RL Training (24-48 hours)
├── SFT Warmup: Base reasoning capabilities
├── GRPO Training: Adaptive teaching skills
└── Output: Teacher model with foundational knowledge
```

| Characteristic      | Detail                                                                    |
| ------------------- | ------------------------------------------------------------------------- |
| Compute requirement | Minimum 2 GPUs (1 for vLLM, 1 for training)                               |
| Training data       | \~900 curated dual-agent demonstrations from Arc-ATLAS-Teach-v0           |
| Foundation domain   | Mathematics (reasoning, sequential thinking, problem decomposition)       |
| Transfer capability | Math-trained reasoning generalizes to debugging, coding, analytical tasks |
| Cost model          | One-time; amortized over all deployments                                  |

### Phase 2: Runtime Continual Learning (Atlas SDK)

The [atlas-sdk](https://github.com/Arc-Computer/atlas-sdk) runtime adapts pre-trained teachers to specific tasks:

```
Runtime Loop (continuous)
├── Task Analysis: Identify performance gaps via rewards
├── Experimentation: Adjust teaching prompts and strategies
├── Trace Export: Capture high-signal interactions
└── Output: Data for next GRPO cycle + incremental improvements
```

| Characteristic | Detail                                            |
| -------------- | ------------------------------------------------- |
| Infrastructure | Managed APIs in SDK                               |
| Speed          | Improves over hours vs full retraining cycles     |
| Safety         | Non-degradation guarantee via reward guardrails   |
| Feedback loop  | Feeds fresh traces into next offline training job |

## Performance Comparison

| Training Approach      | Time to Deploy | Performance Gain | Cost      | Generalization |
| ---------------------- | -------------- | ---------------- | --------- | -------------- |
| Fine-tuning            | 1-2 weeks      | +10-15%          | \$1000s   | Poor           |
| Few-shot prompting     | Minutes        | +3-5%            | \~\$1     | Limited        |
| ATLAS (Runtime + GRPO) | Hours          | +15.7% baseline  | API + GPU | Excellent      |

\*With pre-trained teacher models

## Cross-Domain Transfer Results

### Why Mathematics as Foundation?

* **Clear correctness**: Verifiable ground truth (unlike business tasks)
* **Abundant data**: Thousands of well-structured problems
* **Pure reasoning**: Systematic thinking, problem decomposition, logical flow
* **Complexity gradient**: Simple arithmetic → AIME-level competition problems

Our Teacher trained on \~7,000 math problems achieved **46% accuracy on AIME-25** (top-10 SOTA level).

### Validated Transfer

**Mathematics → Telecom (τ²-bench):**

* Teacher trained only on math problems
* Applied to telecom troubleshooting (no telecom training)
* Result: 24.0% pass\@1 (vs 18.0% for GPT-4.1, Claude 3.7)

**Mathematics → CRM (CRMArena-Pro):**

* Same math-trained teacher
* Applied to policy compliance tasks
* Result: 54% task completion (vs \~35% for leading models)
* Critical accuracy: 69.2% identifying policy violations

→ Full methodology in [Technical Report](/reference/technical-report)

## Quick Start

<Steps>
  <Step title="Offline Foundation">
    Download pre-trained teacher or train custom:

    ```bash theme={null}
    # Option 1: Pre-trained
    huggingface-cli download Arc-Intelligence/ATLAS-8B-Thinking

    # Option 2: Custom (2+ GPUs)
    scripts/launch.sh 2 src/atlas_core/configs/recipe/teacher_sft.yaml
    scripts/launch_with_server.sh 1 1 src/atlas_core/configs/recipe/teacher_rcl.yaml
    ```
  </Step>

  <Step title="Run GRPO on Exported Traces">
    ```bash theme={null}
    atlas-core offline-pipeline \
      --export-path traces/runtime.jsonl \
      --wandb-project atlas-production
    ```
  </Step>

  <Step title="Deploy Enhanced Model">
    Point SDK at new checkpoint:

    ```yaml theme={null}
    teacher:
      llm:
        provider: huggingface
        model: /models/atlas-teacher-grpo
        temperature: 0.2
    ```
  </Step>
</Steps>

## Next Steps

<CardGroup cols="2">
  <Card title="Adaptive Dual-Agent Reasoning" icon="chalkboard-user" href="/concepts/adaptive-dual-agent-reasoning">
    Two-pass dual-agent mechanism
  </Card>

  <Card title="Offline Training Guide" icon="flask" href="/training/offline/grpo-training">
    Run GRPO training pipeline
  </Card>

  <Card title="Training Configuration" icon="diagram-project" href="/training/configuration">
    Hydra composition and overrides
  </Card>

  <Card title="SDK Runtime" icon="layer-group" href="/sdk/quickstart">
    Export traces and manage runtime learning
  </Card>
</CardGroup>

## References

* [ATLAS Technical Report](/reference/technical-report) - Sections 3.1-3.3 on hybrid architecture
* [GRPO Algorithm](https://arxiv.org/abs/2402.03300) - Foundation for offline training
* [SDK Runtime Guide](/sdk/quickstart) - Export traces and continual learning