Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models

Researchers created Apriel-H1 by converting a powerful 15-billion parameter reasoning model into a more efficient Mamba hybrid, achieving a 2.1x throughput increase with minimal quality loss. The breakthrough came from a counterintuitive insight: effective distillation requires using high-quality, multi-step reasoning traces from the original model, not vast pretraining data. This targeted approach succeeds because it preserves the specific and fragile reasoning patterns that get lost in noisy, general-purpose data. The conversion was achieved through a staged process, methodically replacing the original attention layers with Mamba layers based on their importance and training dynamics.