AI

Harness-Aware Distillation for Small Language Model Agents

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 08:18:24 UTC.

Briefings

Researchers propose Harness-Aware Distillation (HAD) for small language model agents, which focuses distillation on teacher-specific abilities beyond the standard harness. Unlike standard distillation that treats the harness as input, HAD contrasts actions with and without harness information to provide unique student insights. The method requires no task rewards, success labels, or future information, and outperforms on-policy distillation baselines across long-horizon benchmarks.

Key points

• Harness-Aware Distillation (HAD) focuses distillation on teacher-specific abilities beyond the standard harness, contrasting actions with and without harness information to provide unique student insights.
• HAD outperforms on-policy distillation baselines across multiple long-horizon agent benchmarks and models, requiring no task rewards, success labels, or future information.

Context

Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback. Standard distillation imitates the teacher's full outputs and treats the harness as part of the input, whereas HAD focuses on what the teacher adds beyond the harness.

Limitations

Evidence covers multiple long-horizon agent benchmarks and models but does not specify which models were tested. Results are based on fixed harness configurations and do not account for variations in harness implementation or external system constraints.

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