Paper page - Orchard: An Open-Source Agentic Modeling Framework

Source: https://huggingface.co/papers/2605.15040 Authors:

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Abstract

Orchard is an open-source framework for scalable agentic modeling that enables training diverse autonomous agents through specialized recipes for coding, GUI navigation, and personal assistance tasks.

Agentic modelingaims to transform LLMs into autonomous agents capable of solving complex tasks throughplanning,reasoning,tool use, andmulti-turn interactionwith environments. Despite major investment, open research remains constrained by infrastructure and training gaps. Many high-performing systems rely on proprietary codebases, models, or services, while most open-source frameworks focus on orchestration and evaluation rather than scalable agent training. We present Orchard, an open-source framework for scalableagentic modeling. At its core is Orchard Env, a lightweightenvironment serviceproviding reusable primitives forsandbox lifecycle managementacross task domains, agent harnesses, and pipeline stages. On top of Orchard Env, we build threeagentic modeling recipes. Orchard-SWE targets coding agents. We distill 107K trajectories from MiniMax-M2.5 and Qwen3.5-397B, introducecredit-assignment SFTto learn from productive segments of unresolved trajectories, and applyBalanced Adaptive Rolloutfor RL. Starting from Qwen3-30B-A3B-Thinking, Orchard-SWE achieves 64.3% onSWE-benchVerified after SFT and 67.5% after SFT+RL, setting a new state of the art among open-source models of comparable size. Orchard-GUI trains a 4B vision-language computer-use agent using only 0.4K distilled trajectories and 2.2K open-ended tasks. It achieves 74.1%, 67.0%, and 64.0% success rates onWebVoyager,Online-Mind2Web, andDeepShop, respectively, making it the strongest open-source model while remaining competitive with proprietary systems. Orchard-Claw targets personal assistant agents. Trained with only 0.2K synthetic tasks, it achieves 59.6% pass@3 onClaw-Evaland 73.9% when paired with a strongerZeroClaw harness. Collectively, these results show that a lightweight, open, harness-agnostic environment layer enables reusable agentic data, training recipes, and evaluations across domains.

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