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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.20257 |
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| _version_ | 1866914288694722560 |
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| author | Ding, Jinren Xu, Xuejian Jiang, Shen Hao, Zhitong Yang, Jinhui Jiang, Peng |
| author_facet | Ding, Jinren Xu, Xuejian Jiang, Shen Hao, Zhitong Yang, Jinhui Jiang, Peng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20257 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding Ding, Jinren Xu, Xuejian Jiang, Shen Hao, Zhitong Yang, Jinhui Jiang, Peng Machine Learning Computer Science and Game Theory 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. |
| title | C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2601.20257 |