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

Paper recorded by Signals 4 on 2026-10-27 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28

Category: cs.LG · 机器学习 · first seen 2026-10-28

Abstract

As executable environments for language agents scale, generating and verifying their rollouts becomes increasingly expensive. Under finite rollout budgets, this raises an important question: can data management at the environment level make agentic reinforcement learning (RL) more efficient by organizing, evaluating, and scheduling task instances together with the environments that determine their

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