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HOBA proposes a hierarchical reinforcement learning framework for online advertising that uses a large language model for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% improvement in a large-scale A/B test.
Proposes a joint optimization framework for multi-slot guaranteed display advertising, addressing slot-level redundancy and contract imbalance via bipartite matching and contract roulette. Online A/B tests on Meituan show significant improvements in revenue and contract fulfillment.