ServiceNow AI researchers have introduced Apriel-H1, a distillation approach that improves how smaller models learn from large reasoning models. Instead of simply copying final outputs, Apriel-H1 identifies which intermediate reasoning steps are most important for solving a problem. This lets the distilled model focus its capacity on the parts of the reasoning chain that matter most.
The method was tested on several reasoning benchmarks, and the resulting small models performed comparably to much larger teacher models. The key insight is that not all reasoning steps contribute equally to the final answer, so selectively transferring the most impactful steps yields better performance than standard distillation.
This work is significant because it suggests a practical way to compress reasoning models without a major accuracy drop. That could make advanced AI reasoning more accessible for edge devices and cost-sensitive applications. The source does not mention any limitations or comparisons to other distillation methods, so the claims should be read as preliminary.