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SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. However, such trajectories are scarce in long-horizon software engineering tasks, where a minor error can invalidate the entire rollout. Repeatedly restart
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