Post-Training on Office Work Boosts Software Engineering Performance
A new study on arXiv (2608.01604) shows that improving a large language model through post-training focused on office tasks can significantly enhance its performance in software engineering. This research introduces the concept of goal-directed execution (GDE), which includes four main behaviors: selecting goals, constructing relevant states, sticking to main objectives, and verifying task completion against the environment. The authors argue that long-horizon post-training boosts these behaviors across different fields. They tested the Qwen3.5-122B-A10B model on 363 tasks related to office workflows, and even though these didn’t include software engineering, the model's pass@1 score on SWE-Bench Pro rose by 5.8 points, indicating improvements in all GDE behaviors, suggesting better overall AI performance.
Key facts
- The study is from arXiv:2608.01604.
- Goal-directed execution (GDE) comprises four behaviors: selecting goals, constructing task-relevant state, maintaining fidelity to higher-level objectives, and verifying completion.
- Qwen3.5-122B-A10B was post-trained on 363 Long-Horizon Multi-Tool Agent (LHMTA) tasks from office workflows.
- The training data contained no software-engineering tasks.
- Pass@1 on SWE-Bench Pro improved by 5.8 points after post-training.
- Matched trajectory analysis showed gains in all four GDE behaviors in both office workflows and software repositories.
- Aggregate SWE-Bench Pro statistics showed changes in information gathering, implementation, and verification.
- The study suggests cross-domain transfer of goal-directed behaviors.
Entities
Institutions
- arXiv