💡 The paper introduces Orchard, an open source framework for scalable agentic modeling, which aims to transform large language models into autonomous agents capable of solving complex tasks. The problem addressed is that current research in agentic modeling is constrained by infrastructure and training gaps, with many high performing systems relying on proprietary codebases, models, or services. Most open source frameworks focus on orchestration and evaluation rather than scalable agent training.

The method presented is the Orchard framework, which consists of a lightweight environment service called Orchard Env, providing reusable primitives for sandbox lifecycle management across task domains, agent harnesses, and pipeline stages. On top of Orchard Env, three agentic modeling recipes are built: Orchard-SWE for coding agents, Orchard-GUI for vision language computer use agents, and Orchard-Claw for personal assistant agents.

The results show that Orchard achieves state of the art performance among open source models of comparable size. Specifically, Orchard-SWE achieves 64.3% and 67.5% on SWE-bench Verified after applying credit assignment and reinforcement learning. Orchard-GUI achieves 74.1%, 67.0%, and 64.0% success rates on WebVoyager, Online-Mind2Web, and DeepShop, respectively. Orchard-Claw achieves 59.6% pass@3 on Claw-Eval and 73.9% when paired with a stronger ZeroClaw harness.

The contributions of the paper are the introduction of the Orchard framework, which enables reusable agentic data, training recipes, and evaluations across domains, and the demonstration of its effectiveness in achieving state of the art performance in various tasks. The paper shows that a lightweight, open, harness agnostic environment layer can enable scalable agentic modeling, making it a significant contribution to the field of artificial intelligence.