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Harness-Aware Distillation for Efficient Small Language Model Agents

October 1, 2026

Harness-Aware Distillation (HAD) optimizes small model agents by focusing distillation on teacher capabilities that exceed the existing software harness. It uses an action preference mechanism to contrast teacher actions with and without harness information, rather than simply imitating full outputs.

HOW THIS AFFECTS YOU

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builderYou can build more capable small-scale agents by specifically targeting the gaps in your existing agentic software stack.

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researcherYou can refine distillation objectives to account for the environmental interfaces (harnesses) used by agents.

read original ↗huggingface.co

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