Environment Evolution for Terminal Agents
Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models become more capable, environments synthesized from scratch become less challenging and thus provide limited learning signals. Recent co-evolution methods iteratively synthesize environments near the model's learnable frontier based on weaknesses exposed during rollouts. However, their dependence on on-policy rollouts limits generalization and the continuous provision of learning signals as the model becomes stronger. In this paper, we propose environment evolution, which incrementally increases environment difficulty off-policy and schedules the evolved environments generation by g...
| Authors | Zhiyuan Fan, Tinghao Yu, Yuanjun Cai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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