Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies, but suffers from two critical limitations: insufficient cross-correlation modeling among state, action, and return-to-go (RTG) sequences, and indiscriminate learning of optimal/suboptimal behaviors. To address these, we propose C2, a novel framework enhancing DT with two core innovations: (1) a Cross Learning Block (CLB) via cross-attention to strengthen inter-sequence correlation modeling; (2) a Constraint-aware Loss (CL) incorporating budget and Cost-Per-Acquisition (CPA) constraints for selective learning of optimal trajectories. Extensive offline evaluations on the AuctionNet dataset demonstrate consistent performance gains (up to 3.2% over state-of-the-art method) across diverse budget settings; ablation studies verify the complementary synergy of CLB and CL, confirming C2's superiority in auto-bidding. The code for reproducing our results is available at: https://github.com/Dingjinren/C2.

翻译：决策Transformer（DT）通过捕捉时序依赖关系在生成式自动出价中展现出潜力，但存在两个关键局限：状态、动作与回报目标序列间跨相关性建模不足，以及对最优/次优行为的不加区分学习。为解决这些问题，我们提出C2——一个通过两项核心创新增强DT的新型框架：（1）通过交叉注意力构建跨学习模块以强化序列间相关性建模；（2）融合预算与单次获取成本约束的约束感知损失函数，实现对最优轨迹的选择性学习。在AuctionNet数据集上的大规模离线评估表明，该框架在不同预算设置下均取得持续性能提升（较现有最优方法最高提升3.2%）；消融实验验证了跨学习模块与约束感知损失的互补协同效应，证实了C2在自动出价任务中的优越性。复现结果的代码已公开于：https://github.com/Dingjinren/C2。