# Pantheon: Personalized Multi-objective Ensemble Sort via Iterative Pareto Policy Optimization

arXiv: https://arxiv.org/abs/2505.13894 · HTML: https://mlsanity.com/paper/2505.13894/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2025.

## Built on by (most cited first, 3 of 3)

- [Generative Optimization for Incentivized Advertising with Global Level Constraints](https://mlsanity.com/paper/2608.04421/) (2026)
- [Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting](https://mlsanity.com/paper/2608.04455/) (2026)
- [DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search](https://mlsanity.com/paper/2608.25635/) (2026)

## Abstract (arXiv)

In this paper, we provide our milestone ensemble sort work and the first-hand practical experience, Pantheon, which transforms ensemble sorting from a "human-curated art" to a "machine-optimized science". Compared with formulation-based ensemble sort, our Pantheon has the following advantages: (1) Personalized Joint Training: our Pantheon is jointly trained with the real-time ranking model, which could capture ever-changing user personalized interests accurately. (2) Representation inheritance: instead of the highly compressed Pxtrs, our Pantheon utilizes the fine-grained hidden-states as model input, which could benefit from the Ranking model to enhance our model complexity. Meanwhile, to reach a balanced multi-objective ensemble sort, we further devise an iterative Pareto policy optimization (IPPO) strategy to consider the multiple objectives at the same time. To our knowledge, this paper is the first work to replace the entire formulation-based ensemble sort in industry RecSys, which was fully deployed at Kuaishou live-streaming services, serving 400 Million users daily.