GUIDE integrates a Decision Transformer for joint modeling of bidding actions and states with Q-value regularization for exploration and an IDM for safe policy fallback, outperforming baselines in simulations and real Taobao deployment with gains in GMV, clicks, cost, and ROI.
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SemBid injects LLM-encoded Task, History, and Strategy semantics as tokens into offline bidding trajectories and uses self-attention to outperform numerical-only baselines in performance, constraint satisfaction, and robustness.
KICL completes execution decisions in KOL financial discourse using offline RL, achieving top returns and Sharpe ratios with no unsupported trades or direction changes on YouTube and X data from 2022-2025.
citing papers explorer
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Generative Auto-Bidding with Unified Modeling and Exploration
GUIDE integrates a Decision Transformer for joint modeling of bidding actions and states with Q-value regularization for exploration and an IDM for safe policy fallback, outperforming baselines in simulations and real Taobao deployment with gains in GMV, clicks, cost, and ROI.
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On the Role of Language Representations in Auto-Bidding: Findings and Implications
SemBid injects LLM-encoded Task, History, and Strategy semantics as tokens into offline bidding trajectories and uses self-attention to outperform numerical-only baselines in performance, constraint satisfaction, and robustness.
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When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse
KICL completes execution decisions in KOL financial discourse using offline RL, achieving top returns and Sharpe ratios with no unsupported trades or direction changes on YouTube and X data from 2022-2025.
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