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

Summary

Retrofitting efficiency into existing large language models often fails when naive distillation disrupts complex multi-step reasoning mechanisms. The Apriel-H1 project addresses this challenge by converting a 15B full-attention reasoning model into a Mamba hybrid using targeted distillation on high-quality SFT reasoning traces rather than diffuse pretraining data. Built on the open-source Fast-LLM framework, the process applies reverse KL divergence alongside a staged layer replacement strategy combining Leave-One-Out analysis and dynamic loss evaluations. Across fifty total blocks, replacing thirty attention layers with Mamba mixers yielded the flagship Apriel-H1-15b-Thinker-SFT checkpoint. This configuration achieved a 2.1x throughput increase with minimal benchmark degradation after 76.8B training tokens.

Takeaways

1. 1

   Distilling reasoning capabilities into linear Mamba hybrids succeeds when training on structured multi-step reasoning traces rather than broad pretraining data.

2. 2

   Reverse KL divergence outperformed forward KL by enforcing mode-seeking behavior that aligns student predictions with high-confidence teacher reasoning paths.

3. 3

   Progressive conversion using Leave-One-Out analysis and dynamic short-step loss evaluations enabled replacing up to 40 of 50 attention layers for up to 3.4x throughput.