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 restarting from the initial state is commonly used yet inefficient, and resetting only the context with working directory misses state changes such as installed dependencies. Therefore, branching from intermediate steps to preserve earlier progress requires restoring both the agent context and execution environments. In this paper, we present SPROUT (SnaPshot-based RollOUT), an agentic RL rollout fram...
| Authors | Mengyang Liu, Weiming Wang, Haixin Nan et al. |
|---|---|
| Primary category | cs.SE |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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