Libra: Efficient Resource Management for
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Libra: Efficient Resource Management for Agentic RL Post-Training
arXiv:2606.03077v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors. In agentic RL, the rollout stage generates trajectories while invoking tools, producing long-tailed and non-stationary workloads that challenge conventional resource-management assumptions. Three fundamental challenges arise.