arXiv AI August 18, 2026 By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

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arXiv:2608. 11980v2 Announce Type: replace-cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

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arXiv AI

6d ago

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs

arXiv:2608. 11980v1 Announce Type: cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.

By Kangning Zhang, Haotian Fang, Xukun Luo, Hao Yin, Yang Gao, Peng Yan, Weiwen Liu, Weinan Zhang, Yong Yu

fine-tuning benchmarks safety

arXiv AI

1d ago

SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation

arXiv:2605. 17648v2 Announce Type: replace Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation.

By Zaiyi Zheng, Liang Wu, Guanghui Min, Yaochen Zhu, Liangjie Hong, Chen Chen, Jundong Li

reinforcement-learning

arXiv Machine Learning

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On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.

By Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju, Donald Loveland, Bhuvesh Kumar, Kijung Shin, Neil Shah, Liam Collins

llms fine-tuning

arXiv AI

Jul 29

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.

By Shutong Qiao, Wei Yuan, Tong Chen, Hao Wang, Quoc Viet Hung Nguyen, Hongzhi Yin

llms efficiency

arXiv Machine Learning

Aug 11

Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation

arXiv:2608. 07816v1 Announce Type: cross Abstract: Recent advances in generative recommendation (GR) leverage large language models (LLMs) as recommender backbones, enabling LLMs to directly generate recommendations conditioned on item-interaction histories.

By Donald Loveland, Liam Collins, Bhuvesh Kumar, Danai Koutra, Neil Shah

llms rag fine-tuning

arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

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llms rag benchmarks

Related stories

HCGRec: Hint-Conditioned Generative Recommendation with Semantic IDs

SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation

Generative Archetype-Grounded Item Representations for Sequential Recommendation