LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents
Researchers have developed LEGO-RL, a framework that improves reinforcement learning for coding agents. Current methods rely on harnesses to manage tool integration and execution feedback, but these harnesses are misaligned with policy-gradient training. LEGO-RL addresses this issue by bridging native coding-agent harnesses with scalable optimization without modifying their internal control flow. The framework consists of three pillars: faithful optimization, reliable executi
Researchers have developed LEGO-RL, a framework that improves reinforcement learning for coding agents. Current methods rely on harnesses to manage tool integration and execution feedback, but these harnesses are misaligned with policy-gradient training. LEGO-RL addresses this issue by bridging native coding-agent harnesses with scalable optimization without modifying their internal control flow. The framework consists of three pillars: faithful optimization, reliable execution, and observable training. It improves performance on several coding tasks, including OpenHands SDK, Claude Code, and OpenCode.
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Why it matters: This matters to researchers in AI because it addresses a significant challenge in reinforcement learning for coding agents, which is crucial for developing more efficient and effective coding tools.
Source: https://arxiv.org/abs/2608.17393
This article was originally published at: https://arxiv.org/abs/2608.17393