Towards Reliable, Scalable, and Value-Aligned Generative Recommendation for Large-Scale Advertising

Yatong Xiao; Yibo Sheng; Lixiang Wang; Mingjie Sun; Yunhao Sha; Pengfei Zhang; Shengyu Wang; Peng Wang

cs.IROct 28, 2026

Towards Reliable, Scalable, and Value-Aligned Generative Recommendation for Large-Scale Advertising

Authors: Yatong Xiao, Yibo Sheng, Lixiang Wang, Mingjie Sun, Yunhao Sha, Pengfei Zhang, Shengyu Wang, Peng Wang,

Organizations: Kuaishou Technology, Beijing, China

Abstract

Generative recommendation (GR) reformulates item retrieval as autoregressive generation of discrete semantic identifiers (SIDs), offering a unified paradigm for personalized recommendation. GR4AD, as an industrial generative recommendation framework, has greatly improved advertising revenue while enabling efficient real-time serving in Kuaishou.However, continued deployment has exposed three limitations: deterministic SID assignment remains sensitive to semantic boundaries; conventional backbone scaling provides diminishing revenue gains under strict serving constraints; and sequence-level value optimization provides limited credit assignment to intermediate SID decisions, causing likelihood-based beam search to prematurely prune potentially high-value candidates. We present GR4AD-X, which addresses these limitations through reliable SID construction, serving-efficient capacity scaling, and unified value alignment. Specifically, GR4AD-X improves SID reliability through Multi-Path assignment. It expands model capacity along multiple complementary dimensions rather than relying solely on wider or deeper backbones, achieving a better effectiveness-efficiency trade-off under strict serving constraints. Finally, Unified Value-Guided Generation and Discrimination (UniVGD) aligns generation with commercial value through prefix-level reward optimization, value-guided beam pruning, and discriminative reranking within a unified model. Large-scale online A/B tests on the Kuaishou advertising platform demonstrate that GR4AD-X achieves a cumulative revenue improvement of 3.07% over the deployed GR4AD baseline while satisfying strict latency and throughput constraints.

Figures & tables

Figure 1. Production trade-off of naive backbone scaling. All variants use Multi-Path SID. Serving QPS and revenue improvements are reported relative to the

0.03

B variant. Larger backbones substantially reduce serving throughput, while revenue gains exhibit diminishing returns.

Figure 2. Overview of GR4AD-X. (a) Multi-Path SID retains multiple plausible first-level assignments for boundary ads. (b) PV-BeamSearch expands to $2k$ candidates and fuses generation and prefix-value ranks via RRF. (c) Revenue rewards propagate bottom-up through the SID prefix tree for PV-RSPO. (d) The backbone uses a Shared Decoder with diagonal Hyper-Connections, a Step Decoder with PCGM, and per-level prefix-value heads for value alignment and discriminative reranking.

Model Settings	$\Delta$ Revenue vs. GR4AD
Reliable SID Construction
+ Multi-Path SID	$+0.58\%$
Efficient Model Scaling
+ Input & Encoder Scaling	$+0.96\%$
+ Vocabulary Scaling (PCGM)	$+1.43\%$
+ Stream Scaling (Diag. HC)	$+1.72\%$

Table 1. Sequential online A/B results for GR4AD-X. Components are added cumulatively, and each row reports the cumulative revenue improvement over the deployed GR4AD baseline.

Figure 3. Geometric motivation and first-level target retention of Multi-Path SID. (a) Distribution of the distance ratio

r=d_{1}/d_{2}

over 7.556M advertisement embeddings with 16,384 first-level centroids. (b) First-level hit rate of Single-Path and Multi-Path SID at

K\in\{1,16,64\}

. Labels above each pair indicate relative improvements over Single-Path SID.

Variant	$\Delta$ Revenue	Parameters	QPS
Multi-Path SID baseline	–	$0.16$ B	$100\%$
+ Input scaling	$+0.20\%$	$0.16$ B	$100\%$
+ Encoder scaling	$+0.38\%$	$0.20$ B	$90\%$
+ Vocabulary scaling	$+0.85\%$	$0.46$ B	$95\%$
+ Stream scaling	$+1.13\%$	$0.49$ B	$90\%$
+ Decision-depth scaling	$+1.41\%$	$0.49$ B	$85\%$

Table 2. Sequential online A/B results of serving-efficient capacity scaling. Each proposed dimension is added on top of the preceding variant. Revenue improvement and QPS are normalized to the

0.16

B Multi-Path SID baseline. The naive scaling result is derived from the same Multi-Path SID-enabled experiments in Figure 1 , re-normalized to the

0.16

B baseline.

	Base Model			+ UniVGD
	Lvl-1	Lvl-1&2	Full	Lvl-1	Lvl-1&2	Full
HR@10	0.017	0.019	0.021	0.145	0.287	0.298
HR@50	0.071	0.075	0.078	0.194	0.329	0.323
HR@100	0.216	0.224	0.221	0.306	0.441	0.427

Table 3. HitRate@

K

against eCPM at three SID scopes. Base uses generation probability; UniVGD uses probability at Lvl-1 and the RRF beam-selection score thereafter. Full excludes final reranking.

Recommender Systems

Recommender systems predict and rank items for people using interaction histories, content, context, and feedback.

See all 521 papers

Generative Recommendation for Large-Scale Advertising
Feb 26, 2026Ben Xue, Dan Liu, Lixiang Wang +27RL for Recommendation Recommender Systems
Kuaishou Technology, Beijing, China
One Model, Two Markets: Bid-Aware Generative Recommendation
Mar 23, 2026Yanchen Jiang, Zhe Feng, Christopher P. Mah +2Recommender Systems Generative Recommendation
Harvard University · Work done during an internship at Google Research · Google Research
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Apr 16, 2026Yanyan Zou, Junbo Qi, Lunsong Huang +7RL for Recommendation Generative Retrieval
JD.com Beijing, China · Waseda University Tokyo, Japan

Feb 26, 2026J/K move · Enter open · S save

Generative Recommendation for Large-Scale Advertising

Ben Xue, Dan Liu, Lixiang Wang, Mingjie Sun+26

Kuaishou Technology, Beijing, China

RL for Recommendation Recommender Systems Generative Recommendation Online Advertising

Open paper AskPDF arXiv

Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generative recommendation in large-scale advertising requires designs beyond large-language-model (LLM)-style training and serving recipes. We present a production-oriented generative recommender co-designed across architecture, learning, and serving, named GR4AD (Generative Recommendation for ADdvertising). As for tokenization, GR4AD proposes UA-SID (Unified Advertisement Semantic ID) to capture complicated business information. Furthermore, GR4AD introduces LazyAR, a lazy autoregressive decoder that relaxes layer-wise dependencies for short, multi-candidate generation, preserving effectiveness while reducing inference cost, which facilitates scaling under fixed serving budgets. To align optimization with business value, GR4AD employs VSL (Value-Aware Supervised Learning) and proposes RSPO (Ranking-Guided Softmax Preference Optimization), a ranking-aware, list-wise reinforcement learning algorithm that optimizes value-based rewards under list-level metrics for continual online updates. For online inference, we further propose dynamic beam serving, which adapts beam width across generation levels and online load to control compute. Large-scale online A/B tests show up to 4.2% ad revenue improvement over an existing DLRM-based stack, with consistent gains from both model scaling and inference-time scaling. GR4AD has been fully deployed in Kuaishou advertising system with over 400 million users and achieves high-throughput real-time serving.

Abstract

Recommender Systems

Explore similar work

Generative Recommendation for Large-Scale Advertising

One Model, Two Markets: Bid-Aware Generative Recommendation

GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation