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arxiv: 2606.27865 · v1 · pith:WJKLH6Z2new · submitted 2026-06-26 · 💻 cs.IR

From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling

Fan Li , Chang Meng , Jiaqi Fu , Shuchang Liu , Tianke Zhang , Xueliang Wang , Xiaoqiang Feng , Yongqi Liu
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Kaiqiao Zhan
This is my paper

Pith reviewed 2026-06-29 02:53 UTC · model grok-4.3

classification 💻 cs.IR
keywords Personalized Landing Page ModelingDecision TransformerSequence ModelingReinforcement LearningUser Behavior ModelingGlobal Local SupervisionOnline A/B Testing
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The pith

A Decision Transformer sequence model with global-local modules replaces CQL reinforcement learning for personalized landing page assignment.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to establish that sequence modeling can overcome two core failures of prior reinforcement learning methods when optimizing which functional page a user first sees upon entering a multi-page app. It shows that the Markov assumption breaks down because user behavior exhibits strong non-Markovian dependencies across days, and that temporal-difference bootstrapping produces accumulating errors and poor credit assignment under delayed rewards. GLAN therefore builds a Decision Transformer that uses an L-RTG module to supply inter-day consumption guidance and an HRM module to supply fine-grained local signals from session feedback. A sympathetic reader would care because the first page choice shapes initial impression and all subsequent engagement, directly affecting retention metrics on large platforms. The approach is validated by online experiments that report gains in daily active users and lifetime value.

Core claim

GLAN is a generative sequence modeling framework built on Decision Transformer that addresses the Markov assumption failure and TD learning cumulative errors of CQL-based KLAN by incorporating the L-RTG module to capture users' inter-day consumption dynamics for global guidance across all page assignments within a day and the HRM module to decompose session-level feedback into fine-grained signals for precise local supervision of each page assignment.

What carries the argument

L-RTG and HRM modules inside a Decision Transformer that jointly supply global inter-day guidance and local session-level supervision for page assignment decisions.

If this is right

  • Non-Markovian temporal dependencies across days are handled directly rather than approximated by state augmentation.
  • Credit assignment for delayed rewards improves because the model conditions on full trajectories instead of bootstrapped value estimates.
  • Global guidance from inter-day patterns and local supervision from session decomposition can be applied together in one architecture.
  • Production deployment yields measurable lifts of 0.158 percent in DAU and 0.108 percent in LT.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same global-local decomposition could be tested on other long-horizon recommendation tasks that currently rely on bootstrapped RL.
  • If the modules prove separable, one could measure whether inter-day or intra-session signals contribute more to the observed gains.
  • Platforms with similar entry patterns but different reward delays could adopt the architecture without re-deriving a new RL objective.

Load-bearing premise

The L-RTG module accurately captures inter-day consumption dynamics and the HRM module successfully decomposes session-level feedback into fine-grained signals that provide reliable global and local supervision.

What would settle it

An online A/B test in which GLAN produces no increase or a decrease in DAU and LT relative to the prior CQL method would falsify the claim that the sequence modeling approach improves outcomes.

Figures

Figures reproduced from arXiv: 2606.27865 by Chang Meng, Fan Li, Jiaqi Fu, Kaiqiao Zhan, Shuchang Liu, Tianke Zhang, Xiaoqiang Feng, Xueliang Wang, Yongqi Liu.

Figure 1
Figure 1. Figure 1: An example of the interaction paradigm in PLPM: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) The observed log-linear correlation between daily app usage time and session frequency; (b) the comparison [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The architecture of our proposed method. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the online pipeline. (RTG) prediction. We will publicly release this large-scale real￾world benchmark to promote further research in this area upon paper acceptance. Specifically, each instance includes three cate￾gories of features: (1) user profile attributes such as age, region, and activity level; (2) statistical consumption features, including general consumption statistics (e.g., app … view at source ↗
Figure 5
Figure 5. Figure 5: Online A/B test experimental results of DAU and LT. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cases of different pages on Kuaishou APP. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Modern online platforms increasingly adopt multi-page architectures to accommodate diverse user needs. On these platforms, page navigation (the process of directing users to specific functional pages upon app entry) serves as a critical gateway that shapes user's first impression and significantly influences subsequent engagement. To optimize this process, Kuaishou formulated the task of Personalized Landing Page Modeling (PLPM) and proposed KLAN, a reinforcement learning framework built upon Conservative Q-Learning (CQL). However, CQL-based approaches suffer from two fundamental limitations: (1) the Markov assumption fails to capture the strong non-Markovian temporal dependencies inherent in real-world user behaviors, and (2) TD learning with bootstrapping incurs severe cumulative errors and credit assignment difficulties under delayed rewards, particularly in long-horizon settings where users enter the app multiple times daily. To address these limitations, we propose GLAN (Generative Landing-page Adaptive Navigator), a sequence modeling framework built on Decision Transformer to tackle PLPM from a unified global-local perspective. Specifically, GLAN incorporates two key modules. First, we design the L-RTG module that captures users' inter-day consumption dynamics to provide accurate global guidance for all page assignments within a day. Furthermore, we propose the HRM module that decomposes session-level feedback into fine-grained signals, enabling precise local supervision for each page assignment. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of GLAN, achieving +0.158\% and +0.108\% improvements on Daily Active Users (DAU) and user Lifetime (LT) respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes GLAN, a Decision Transformer-based sequence modeling framework for Personalized Landing Page Modeling (PLPM) on the Kuaishou platform. It critiques the prior CQL-based KLAN for failing the Markov assumption on non-Markovian user behaviors and suffering cumulative TD errors under delayed rewards, then introduces L-RTG to capture inter-day consumption dynamics for global guidance and HRM to decompose session feedback for local supervision. Online A/B tests report +0.158% DAU and +0.108% LT lifts.

Significance. If the online results hold under rigorous controls, the work demonstrates a practical shift from bootstrapped RL to generative sequence modeling for long-horizon recommendation tasks, potentially improving credit assignment in multi-session user journeys. The explicit global-local decomposition via L-RTG and HRM offers a concrete architectural pattern that could generalize beyond landing-page navigation.

major comments (1)
  1. [Experiments section] Experiments section: the abstract and results report +0.158% DAU and +0.108% LT gains from online A/B tests, yet provide no information on the control baseline (e.g., KLAN or production heuristic), experiment duration, number of users, statistical significance tests, or how L-RTG and HRM were deployed in the live system. These omissions are load-bearing because the central claim—that the modules resolve the stated Markov and TD limitations—rests entirely on the attribution of these lifts.
minor comments (1)
  1. [§3] Notation for L-RTG and HRM is introduced without an explicit equation or pseudocode block showing how the return-to-go tokens and hierarchical reward signals are constructed from raw logs.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the experiments section. We agree that additional details are needed to support the attribution of the reported online gains and will revise accordingly.

read point-by-point responses

  1. Referee: [Experiments section] Experiments section: the abstract and results report +0.158% DAU and +0.108% LT gains from online A/B tests, yet provide no information on the control baseline (e.g., KLAN or production heuristic), experiment duration, number of users, statistical significance tests, or how L-RTG and HRM were deployed in the live system. These omissions are load-bearing because the central claim—that the modules resolve the stated Markov and TD limitations—rests entirely on the attribution of these lifts.

    Authors: We acknowledge the omission of these critical experimental details in the current manuscript. In the revised version, we will add a new subsection in the Experiments section that explicitly describes: the control baseline (the production system using the prior KLAN model), the A/B test duration and user scale, the statistical significance testing procedure and resulting p-values for the DAU and LT lifts, and the online deployment architecture showing how L-RTG and HRM are integrated into the live serving pipeline. These additions will directly address the concern about attribution and allow readers to evaluate the strength of the evidence. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper motivates GLAN by critiquing limitations of prior CQL-based KLAN (Markov assumption failure and TD error accumulation) but validates effectiveness solely through external online A/B experiments on the Kuaishou platform reporting +0.158% DAU and +0.108% LT lifts. No equations, modules (L-RTG, HRM), or derivations are presented that reduce predictions or results to self-fitted parameters, self-definitional quantities, or load-bearing self-citations. The sequence modeling approach on Decision Transformer is positioned as an alternative framework whose value is measured by live user metrics, not internal consistency checks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5844 in / 1143 out tokens · 51540 ms · 2026-06-29T02:53:19.143781+00:00 · methodology

discussion (0)

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Reference graph

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