AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
Terminal agents solve tasks in software engineering and scientific research by executing commands in a terminal. Training them via reinforcement learning (RL) requires diverse and verifiable environments, but these environments are still mostly designed by human researchers, so their supply grows only as fast as researchers can work. We introduce AutoEnvScaling, which treats environment design as a terminal task and uses agents to automate the data flywheel. A proposer agent builds new RL environments from the rollouts of the solver being trained, and the solver's new rollouts guide the next round. By turning environment generation into a terminal task, AutoEnvScaling increases the valid-...
| Authors | Simon Yu, Baolin Peng, Bo Liu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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