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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 runtime.

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Full Report

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Access the complete 28-page technical report with detailed methodology, experiments, and results

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Key Contributions

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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.

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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.

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3. Compounding Intelligence

Demonstrated skill transfer across domains with up to 83% transfer efficiency, creating accumulating knowledge over time.

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4. Safety Guarantees

Zero-reward for performance degradation ensures 97% non-degradation rate in production deployments.

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Experimental Results

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Performance Across Model Sizes

Student ModelSizeBaselinew/ ATLASImprovement
Qwen3-4B4B62.3%78.0%+15.7%
Llama-3.1-8B8B71.2%85.4%+14.2%
Mixtral-8x7B47B78.5%89.1%+10.6%
GPT-4~1.7T84.3%92.8%+8.5%

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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

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Citation

If you use ATLAS in your research, please cite: 
@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}
}
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

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Next Steps

Models

Pre-trained ATLAS models

Datasets

Training and evaluation data

Implementation

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Examples

See ATLAS in action