  Auto-bidding plays a crucial role in facilitating online advertising by
automatically providing bids for advertisers. Reinforcement learning (RL) has
gained popularity for auto-bidding. However, most current RL auto-bidding
methods are modeled through the Markovian Decision Process (MDP), which assumes
the Markovian state transition. This assumption restricts the ability to
perform in long horizon scenarios and makes the model unstable when dealing
with highly random online advertising environments. To tackle this issue, this
paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding
through generative modeling. In this paradigm, we propose DiffBid, a
conditional diffusion modeling approach for bid generation. DiffBid directly
models the correlation between the return and the entire trajectory,
effectively avoiding error propagation across time steps in long horizons.
Additionally, DiffBid offers a versatile approach for generating trajectories
that maximize given targets while adhering to specific constraints. Extensive
experiments conducted on the real-world dataset and online A/B test on Alibaba
advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81%
increase in GMV and 3.36% increase in ROI.
