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Long-horizon post-training boosts software engineering performance by enhancing goal-directed execution behaviors, even when the training domain is unrelated.
Language models struggle to follow long, binding policies, with top configurations passing only 36.2% of trials in a rigorous benchmark simulating real-world tasks.
