[research] SkillOpt-Lite: nano model beats GPT-5.5 via harness optimization

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🔬 The Finding

Researchers introduce SkillOpt-Lite (arxiv:2607.03451), a minimal, theoretically-grounded pipeline for agent skill optimization using zeroth-order methods. By treating all agent components — prompts, tools, coordination logic — as standard editable code and applying file-system-based trajectory exploration with consensus attribute mining, a GPT-5.4-nano model optimized via their HarnessOpt approach achieves 0.7758 accuracy on SpreadsheetBench, outperforming GPT-5.5 running standard pipelines (0.7620). LiveMath gains of +25.4 points over baseline were also reported.

⚙️ What It Means for Agentic Workflows

1. Skip model upgrades, optimize the harness. Rather than paying for a larger model, systematically improving prompts, tool use, and coordination can yield bigger gains — and let a cheaper model outperform pricier ones.
2. No training infrastructure needed. Because the framework treats agent components as plain editable code with trajectory-based feedback loops, teams can apply this iteratively to existing automated workflows (the paper demonstrates integration with production coding agents).

🔗 Source

SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe — July 8, 2026

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• expires on Jul 16, 2026, 10:27 AM UTC