4 papers
Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following
Da Guo, Shijia Wang, Qiang Xiao +7
Generative retrieval has emerged as a promising paradigm in recommender systems, offering superior sequence modeling capabilities over traditional dual-tower architectures. However…
Limited Reasoning Space: The cage of long-horizon reasoning in LLMs
Zhenyu Li, Guanlin Wu, Cheems Wang +1
The test-time compute strategy, such as Chain-of-Thought (CoT), has significantly enhanced the ability of large language models to solve complex tasks like logical reasoning. Howev…
FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Xianwen Guo, Bin Huang, Xiaomeng Wu +6
Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase…
Climber: Toward Efficient Scaling Laws for Large Recommendation Models
Songpei Xu, Shijia Wang, Da Guo +5
Transformer-based generative models have achieved remarkable success across domains with various scaling law manifestations. However, our extensive experiments reveal persistent ch…