Arctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design

Long-horizon agent training, including software engineering, is advancing rapidly, but the recipe and infrastructure needed for a strong reinforcement-learning baseline remain difficult to reproduce. This makes gains from new algorithms or data hard to distinguish from improvements to an under-tuned baseline. We present Arctic-SWE, a fully asynchronous RL framework that establishes such a baseline through model-system co-design, using overfitting on a small task pool as the testbed. This testbed exposes failures that early reward gains conceal and guides the integration of established components: gated reward, importance-weight control, aligned training and inference engines, and consistent state across policy updates. No single component is new, yet together they form the missing foundation. On raw R2E-Gym data, without new algorithms or data work, Arctic-SWE trains Qwen3.5-4B to 62.0% pass@1 on SWE-bench Verified. This is comparable to concurrent FrogNano (61.5%), which relies on online task synthesis, after about one-sixth as many training rollouts. Building on this foundation, we further filter and refine R2E-Gym, SWE-rebench V2, and Scale-SWE. With this curated subset, the 4B model improves from 47.4%/25.0%/37.9% to 65.0%/38.3%/59.7% on SWE-bench Verified/Pro-v1/Pro-v2, and the recipe also improves Qwen3.5-9B from 56.2%/31.9%/48.8% to 65.7%/39.6%/70.2% on Verified/Pro-v1/Pro-v2. Controlled comparisons show that both curation and test strengthening improve held-out performance, although stronger tests reduce apparent training-task mastery. Arctic-SWE provides a documented starting point for studying better algorithms, data and environments for asynchronous agent RL.