C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding

• URL: http://arxiv.org/abs/2601.20257v2
• Date: Thu, 29 Jan 2026 10:08:33 GMT
• Title: C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding
• Authors: Jinren Ding, Xuejian Xu, Shen Jiang, Zhitong Hao, Jinhui Yang, Peng Jiang,
• Abstract summary: Decision Transformer (DT) shows promise for generative auto-bidding by capturing temporal dependencies.<n>DT suffers from insufficient cross-correlation modeling among state, action, and return-to-go sequences.<n>We propose C2, a novel framework enhancing DT with two core innovations.
• Score: 9.446373834962895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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