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Peng Jiang 0002

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4 papers
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4

ICML Conference 2025 Conference Paper

Learning Cascade Ranking as One Network

  • Yunli Wang
  • Zhen Zhang
  • Zhiqiang Wang
  • Zixuan Yang
  • Yu Li
  • Jian Yang 0003
  • Shiyang Wen
  • Peng Jiang 0002

Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i. e. , end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for different stages. To address these challenges, we propose LCRON, which introduces a novel surrogate loss function derived from the lower bound probability that ground truth items are selected by cascade ranking, ensuring alignment with the overall objective of the system. According to the properties of the derived bound, we further design an auxiliary loss for each stage to drive the reduction of this bound, leading to a more robust and effective top-k selection. LCRON enables end-to-end training of the entire cascade ranking system as a unified network. Experimental results demonstrate that LCRON achieves significant improvement over existing methods on public benchmarks and industrial applications, addressing key limitations in cascade ranking training and significantly enhancing system performance.

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ICML Conference 2025 Conference Paper

Learning Monotonic Probabilities with a Generative Cost Model

  • Yongxiang Tang 0001
  • Yanhua Cheng
  • Xiaocheng Liu
  • Jiaochen Chen
  • Yanxiang Zeng
  • Ning Luo 0004
  • Pengjia Yuan
  • Xialong Liu

In many machine learning tasks, it is often necessary for the relationship between input and output variables to be monotonic, including both strictly monotonic and implicitly monotonic relationships. Traditional methods for maintaining monotonicity mainly rely on construction or regularization techniques, whereas this paper shows that the issue of strict monotonic probability can be viewed as a partial order between an observable revenue variable and a latent cost variable. This perspective enables us to reformulate the monotonicity challenge into modeling the latent cost variable. To tackle this, we introduce a generative network for the latent cost variable, termed the Generative Cost Model ( GCM ), which inherently addresses the strict monotonic problem, and propose the Implicit Generative Cost Model ( IGCM ) to address the implicit monotonic problem. We further validate our approach with a numerical simulation of quantile regression and conduct multiple experiments on public datasets, showing that our method significantly outperforms existing monotonic modeling techniques. The code for our experiments can be found at https: //github. com/tyxaaron/GCM.

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ICML Conference 2025 Conference Paper

Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads

  • Siqi Kou
  • Jiachun Jin
  • Zhihong Liu
  • Chang Liu
  • Ye Ma
  • Jian Jia
  • Quan Chen 0006
  • Peng Jiang 0002

We introduce Orthus, a unified multimodal model that excels in generating interleaved images and text from mixed-modality inputs by simultaneously handling discrete text tokens and continuous image features under the AR modeling principle. The continuous treatment of visual signals minimizes the information loss while the fully AR formulation renders the characterization of the correlation between modalities straightforward. Orthus leverages these advantages through its modality-specific heads—one regular language modeling (LM) head predicts discrete text tokens and one diffusion head generates continuous image features. We devise an efficient strategy for building Orthus—by substituting the Vector Quantization (VQ) operation in the existing unified AR model with a soft alternative, introducing a diffusion head, and tuning the added modules to reconstruct images, we can create an Orthus-base model effortlessly (e. g. , within 72 A100 GPU hours). Orthus-base can further embrace post-training to craft lengthy interleaved image-text, reflecting the potential for handling intricate real-world tasks. For visual understanding and generation, Orthus achieves a GenEval score of 0. 58 and an MME-P score of 1265. 8 using 7B parameters, outperforming competing baselines including Show-o and Chameleon.

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ICLR Conference 2023 Conference Paper

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor

  • Wanqi Xue
  • Qingpeng Cai 0001
  • Ruohan Zhan
  • Dong Zheng
  • Peng Jiang 0002
  • Kun Gai
  • Bo An 0001

Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation. However, due to expensive online interactions, it is very difficult for RL algorithms to perform state-action value estimation, exploration and feature extraction when optimizing long-term engagement. In this paper, we propose ResAct which seeks a policy that is close to, but better than, the online-serving policy. In this way, we can collect sufficient data near the learned policy so that state-action values can be properly estimated, and there is no need to perform online exploration. ResAct optimizes the policy by first reconstructing the online behaviors and then improving it via a Residual Actor. To extract long-term information, ResAct utilizes two information-theoretical regularizers to confirm the expressiveness and conciseness of features. We conduct experiments on a benchmark dataset and a large-scale industrial dataset which consists of tens of millions of recommendation requests. Experimental results show that our method significantly outperforms the state-of-the-art baselines in various long-term engagement optimization tasks.

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