EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning

Sihan Yang, Weichen Bai, Ruichuan An, Yiming Cheng, Zhiyuan Feng et al.|October 27, 2026arXiv

Key Takeaway

By treating environments as first-class scheduling objects and predicting reward variance across task-environment pairs, EnvSched achieves 3x faster training than random selection—reaching in 40 steps what random sampling needs 120 steps to accomplish.

Summary

EnvSched is a scheduling system that makes reinforcement learning for language agents more efficient by intelligently selecting which tasks to train on within limited rollout budgets.

training efficiency agents

Key Terms

rollout-diversity reward-variance-prediction environment-task-graph exploitation-coverage-tradeoff