// HeavySkill // One of the cleaner takes on agentic harness design I've read. They argue that what actually drives agent harness performanc…

// HeavySkill // One of the cleaner takes on agentic harness design I've read. They argue that what actually drives agent harness performance is not the orchestration code. It's a single inner skill: parallel reasoning followed by deliberation. If you can internalize that into the model and most of the scaffolding becomes optional. The paper systematizes this as a two-stage pipeline you can run beneath any harness, then trains it as a learnable skill via RLVR. The numbers: > GPT-OSS-20B jumps from 69.7% (M@K) to 85.5% (HM@4) on LiveCodeBench under the heavy-thinking variant. > R1-Distill-Qwen-32B nearly doubles on IFEval, from 35.7% to 69.3%. > Several models reach Pass@N-level performance with HeavySkill. Harness wins start to look like model wins once you can train them in. If parallel-reasoning-plus-deliberation really is the inner skill, the long arc is models that come with it baked in, not orchestration glue around them. Paper: https://arxiv.org/abs/2605.02396 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-05-05