AI RESEARCH
Efficient Hyperparameter Optimization for LLM Reinforcement Learning
arXiv CS.LG
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ArXi:2606.03073v1 Announce Type: new Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive. Existing multi-fidelity HPO methods remain inefficient for LLM RL due to the massive model scale and resource-intensive