SPROUT runs coding attempts inside microVMs and incrementally snapshots the full filesystem after each assistant turn, including edited files, installed dependencies and build artifacts.
Intermediate snapshots improve long-horizon coding agents at lower cost
SPROUT restores both execution environments and conversation context, letting agents branch from failed attempts instead of restarting.
Independent
Mengyang Liu · Weiming Wang · Haixin Nan · Yu Wu · Peize Li · Shihong Huang · +3 more
Research Digest··2 min read
The authors present SPROUT, a reinforcement-learning rollout framework for software tasks that can take many steps or hours to complete.
Why this paper
Independent
In one line
Checkpointing agent state after each turn and resuming failed rollouts via verifier-guided backtracking makes long-horizon RL for coding agents more effective and cheaper.
What we could check
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- ✓Reports numbers on named benchmarks (3 benchmarks)
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