Post-Training on Office Work Improves Software Engineering: A Behavioral Account of Cross-Domain Transfer

Abstract:Long-horizon tasks require agents to maintain coherent state and goals across nested and branching work. We call this capability goal-directed execution (GDE): the repeated application of four behaviors, namely selecting goals, constructing task-relevant state, maintaining fidelity to higher-level objectives, and verifying completion against the environment. We hypothesize that long-horizon post-training strengthens these behaviors across domains. We test this by post-training Qwen3.5-122B-A10B on 363 Long-Horizon Multi-Tool Agent (LHMTA) tasks drawn from office workflows. The collection contained no software-engineering tasks, yet the model's pass@1 improved by 5.8 points on SWE-Bench Pro. Matched trajectory analysis shows gains in all four GDE behaviors in both office workflows and software repositories. Aggregate SWE-Bench Pro statistics showed related changes in information gathering, implementation, and verification. Together, the results support a behavioral interpretation in which long-horizon post-training changed how the model organized and applied knowledge across tasks, with effects extending beyond the training domain.

Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2608.01604 [cs.AI]

(or arXiv:2608.01604v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.01604 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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