Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding

• URL: http://arxiv.org/abs/2607.24232v1
• Date: Mon, 27 Jul 2026 10:08:43 GMT
• Title: Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding
• Abstract summary: SAGE is a novel Strategy-aware Auto-bidding framework guided by LLMs for efficient bidding.<n>We show that SAGE consistently achieves superior performance while tuning less than 10% of the trainable parameters required by full fine-tuning.
• Score: 16.834816807572757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advertising bidding has evolved from manual strategies to auto-bidding systems better adapted for large-scale, dynamic auction environments. While recent advances in Large Language Models (LLMs) offer strong reasoning for auto-bidding, existing methods suffer from shallow trajectory-text interactions and require costly fine-tuning, hindering the efficient use of pretrained knowledge under diverse constraints. To address these challenges, we propose SAGE, a novel Strategy-aware Auto-bidding framework Guided by LLMs for Efficient bidding. SAGE introduces a parameter-efficient multi-modal alignment framework for constrained auto-bidding with LLMs. Specifically, SAGE comprises three key components: (i) the position augmentation module adopts temporal-semantic positional embeddings to effectively capture the intrinsic dynamics and semantic structures; (ii) the text alignment module leverages gated cross-attention to align the embedding spaces of trajectory and text modalities, enabling effective multi-modal fusion while alleviating the computational overhead caused by long trajectories; (iii) the constraint-gated LoRA module employs constraints as routing signals, activating only a small subset of experts to adapt the behavior of a frozen LLM efficiently. Extensive experiments on large-scale auto-bidding benchmark demonstrate that SAGE consistently achieves superior performance while tuning less than 10% of the trainable parameters required by full fine-tuning. Ablation studies further validate the critical contribution of each component to the framework's overall performance.

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