> ## 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. # Technical Report > ATLAS research paper and technical specifications ## Abstract ATLAS (Adaptive Teaching and Learning Alignment System) is a hybrid reinforcement learning architecture that enhances language model performance through an adaptive dual-agent reasoning loop. The system pairs your production agent (the student) with a verifying teacher that first diagnoses capability via a lightweight probe, then provides targeted guidance and certifications before answers ship. Through extensive evaluation on mathematical reasoning, code generation, and system reliability engineering tasks, ATLAS demonstrates: * **Closed-loop runtime gains**: +15.7% average accuracy, +31% completion, 97% non-degradation, \~50% token savings * **Offline GRPO gains**: sustained quality improvements when fine-tuning custom teacher checkpoints from production traces The framework combines offline reinforcement learning for foundational skills with runtime orchestration that keeps quality high in production. Task-specific continual learning is now delivered through the [`atlas-sdk`](https://github.com/Arc-Computer/atlas-sdk) runtime. ## Full Report Access the complete 28-page technical report with detailed methodology, experiments, and results ## Key Contributions ### 1. Adaptive Dual-Agent Protocol A two-pass inference mechanism that first diagnoses student capability (≤50 tokens) then provides calibrated verifying-teacher guidance (≤200 tokens) based on the assessment. ### 2. Hybrid Learning Architecture Separation of expensive offline RL training from the managed runtime that captures production traces, enabling rapid adaptation without retraining base student models. ### 3. Compounding Intelligence Demonstrated skill transfer across domains with up to 83% transfer efficiency, creating accumulating knowledge over time. ### 4. Safety Guarantees Zero-reward for performance degradation ensures 97% non-degradation rate in production deployments. ## Experimental Results ### Performance Across Model Sizes | Student Model | Size | Baseline | w/ ATLAS | Improvement | | ------------- | ------ | -------- | -------- | ----------- | | Qwen3-4B | 4B | 62.3% | 78.0% | +15.7% | | Llama-3.1-8B | 8B | 71.2% | 85.4% | +14.2% | | Mixtral-8x7B | 47B | 78.5% | 89.1% | +10.6% | | GPT-4 | \~1.7T | 84.3% | 92.8% | +8.5% | ### Domain-Specific Gains * **SRE Debugging**: Systematic improvement in root cause analysis and reduced investigation time * **Mathematical Reasoning**: 15.7% average gain (closed-loop baseline) * **Code Generation**: 31% completion rate improvement * **Continual Learning (SDK)**: Use the atlas-sdk runtime for rapid, task-specific adaptation between offline training runs ## Citation If you use ATLAS in your research, please cite: ```bibtex theme={null} @article{atlas2024, title={ATLAS: Adaptive Teaching and Learning Alignment System for RL}, author={Arc Intelligence Team}, journal={arXiv preprint}, year={2024}, url={https://github.com/Arc-Computer/ATLAS} } ``` ## Related Work The ATLAS framework builds on several foundational works: * GRPO (Group Relative Policy Optimization) for RL training * Genetic prompt evolution research for online optimization, now implemented and maintained in the atlas-sdk runtime * Constitutional AI principles for safe deployment ## Next Steps Pre-trained ATLAS models Training and evaluation data Get started with ATLAS See ATLAS in action