Interactor: Agentic RL oriented Iterative Creation for Ad Description Generation in Sponsored Search
arXiv:2606.15911v1 Announce Type: new Abstract: This paper focuses on automatically generating informative ad descriptions in sponsored search. Unlike ad titles which are usually optimized to attract user click feedbacks, ad descriptions have a longer text span and possess the potential of incorporating world knowledge to address user search intents while presenting the fine-grained selling points of the ads. We propose Interactor, a multi-turn iterative creation framework optimized with agentic RL for ad description generation. The generation model acts as a policy that interacts with a custo
The paper leverages recent advancements in agentic reinforcement learning to tackle the challenge of generating more effective ad descriptions, a long-standing objective in digital advertising.
Improved automated ad description generation, especially with agentic RL, can significantly enhance the efficiency and effectiveness of sponsored search, leading to better ROI for advertisers and more relevant results for users.
Ad descriptions can become more contextually aware, incorporate broader knowledge, and be optimized for user intent beyond simple click-through rates.
- · Digital advertising platforms
- · Advertisers
- · Consumers (through more relevant ads)
- · Copywriters specializing in basic ad descriptions
More sophisticated and engaging ad descriptions are automatically generated, improving campaign performance.
Increased reliance on AI for creative aspects of advertising may shift talent needs in marketing departments.
The technology could be adapted to other forms of content generation where 'world knowledge' and iterative optimization are crucial.
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Read at arXiv cs.CL