HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness
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| Authors | Jianing Wang et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2605.02396 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarkable success in complex reasoning tasks. However, the underlying mechanism that truly drives performance remains obscured behind intricate system designs. In this paper, we propose HeavySkill, a perspective that views heavy thinking not only as a minimal execution unit in orchestration harness but also as an inner skill internalized within the model's parameters that drives the orchestrator to solve complex tasks. We identify this skill as a two-stage pipeline, i.e., parallel reasoning then summarization, which can operate beneath any agentic harness. We present a systematic empirical study of HeavySkill across diverse domains. Our results show that this inner skill consistently outperforms traditional Best-of-N (BoN) strategies; notably, stronger LLMs can even approach Pass@N performance. Crucially, we demonstrate that the depth and width of heavy thinking, as a learnable skill, can be further scaled via reinforcement learning, offering a promising path toward self-evolving LLMs that internalize complex reasoning without relying on brittle orchestration layers.
Engineering Breakdown
Plain English
This paper argues that the real performance driver in multi-agent orchestration systems isn't the complex coordination framework itself, but rather an internal 'heavy thinking' capability that models learn—structured as parallel reasoning followed by summarization. The authors empirically validate that this two-stage pipeline pattern exists across different domains and can work underneath any agentic harness architecture.
Key Engineering Insight
Heavy thinking isn't just a runtime execution pattern—it's an internalized skill baked into model parameters that orchestrators naturally learn. This means you don't need to engineer perfect coordination logic; the model learns to do parallel reasoning then compress results, and this works regardless of your framework choice.
Why It Matters for Engineers
Engineers building agentic systems have been overcomplicating orchestration frameworks, adding memory management, skill registries, and tool APIs. If heavy thinking is the actual driver, you can simplify your infrastructure and focus on ensuring models have capacity for this reasoning pattern. This directly impacts system latency, cost, and maintainability in production deployments.
Research Context
Prior work assumed multi-agent performance came from sophisticated orchestration—memory handling, tool composition, agent specialization. This paper inverts that assumption: the orchestration is almost secondary to whether the underlying model can learn and execute this two-stage reasoning pattern. It opens a path to simpler, more efficient agentic systems by understanding the fundamental mechanism that actually makes them work.
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