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