SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
What this work does
SPROUT checkpoints both execution state and matching agent context at turn boundaries, then branches failed attempts through verifier-guided continuation selection.
Connection to discovery
A concrete harness requirement for reusing partial experimental progress during long-horizon RL.
Evidence
The body distinguishes environmental snapshots from transcript reuse and describes a joint-state archive, while software-engineering comparisons report success and rollout cost.
Scope and limitations
A physical laboratory generally cannot be restored like a filesystem. Branched data also require an explicit sampling and loss policy; simply concatenating successful suffixes is not automatically on-policy.
THE RESEARCH CONVERSATION
Discussion & reactions
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