HeavySkill
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One of the cleaner takes on agentic harness design released this year. The paper argues that what actually drives harness performance is not the orchestration code, but a single inner skill: parallel reasoning followed by deliberation. Internalize that pattern into the model and most of the surrounding scaffolding becomes optional. HeavySkill systematizes the idea as a two-stage pipeline you can run beneath any harness, then trains it as a learnable skill via RLVR. The result is a harness win that looks more like a model win.
Two-stage skill, not orchestration glue: Stage one runs parallel reasoning across multiple sampled chains. Stage two performs a deliberation pass that compares, critiques, and synthesizes those chains into a final answer. The pipeline is the same regardless of harness, which is why it transfers across tasks.
GPT-OSS-20B jumps from 69.7% to 85.5% on LiveCodeBench: Under the heavy-thinking variant (HM@4), the 20B model gets a 15.8 point lift on a hard coding benchmark. The same recipe takes R1-Distill-Qwen-32B from 35.7% to 69.3% on IFEval, nearly doubling its instruction-following score.
Pass@N-level performance from a learned skill: Several models reach Pass@N-level performance once HeavySkill is internalized through RLVR, which is the property that makes the parallel-deliberation pattern actually portable. The skill survives outside the harness it was trained under.
Why it matters: 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 ship with it baked in, not orchestration glue layered around them.
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