mlsanityData to 14 Oct 2026

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

arXiv 2601.20257 ↗First posted 28 Jan 2026cs.LG cs.GTcited by 1

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 29 references that are in the map. First posted on arXiv 28 Jan 2026.

Builds on

13 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Decision Transformer: Reinforcement Learning via Sequence Modeling2 Jun

Other references · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  5. 2017Online Convex Optimization with Stochastic Constraintscited by 95
  6. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 91
  7. 2019DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systemscited by 44
  8. 2018Deep Reinforcement Learning for Sponsored Search Real-time Biddingcited by 34
  9. 2024AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Gamescited by 28
  10. 2020Jointly Learning to Recommend and Advertisecited by 27
  11. 2018Deep reinforcement learning for search, recommendation, and online advertising: a surveycited by 23
  12. 2025Generative Auto-Bidding with Value-Guided Explorationscited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the Role of Language Representations in Auto-Bidding: Findings and Implicationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding
  2. 2025Optimal Return-to-Go Guided Decision Transformer for Auto-Bidding in Advertisement
  3. 2026DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
  4. 2025EBaReT: Expert-guided Bag Reward Transformer for Auto Bidding
  5. 2026OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios
  6. 2025Expert-Guided Diffusion Planner for Auto-Bidding
  7. 2025Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
  8. 2023HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning