arXiv AI

Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

arXiv:2608. 06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior.

arXiv AI

Jul 7

Best-of-Better-$N$: Generating Pre-Aligned Responses with In-Context Learning

arXiv:2607. 03453v1 Announce Type: cross Abstract: Inference-time alignment methods, such as Best-of-$N$, offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by a reference LLM.

By Eric Lei, Hsiang Hsu, Chun-Fu Chen

llms benchmarks safety

arXiv Machine Learning

Jul 31

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

arXiv:2604. 07343v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.

By Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou, Junshan Zhang, Zhe Zhao

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 2

Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models

arXiv:2604. 17415v3 Announce Type: replace-cross Abstract: Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model.

By Jeongjae Lee, Jinho Chang, Jeongsol Kim, Jong Chul Ye

diffusion fine-tuning safety

arXiv Machine Learning

Jul 17

Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

arXiv:2607. 14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention.

By Dingsu Wang, Filip Ryzner, Kelly He, Armando Ordorica, David Woo, Aditya Mantha, Liyao Lu, Usha Amrutha Nookala, Haoran Guo, Jiacong He, Olafur Gudmundsson, Matt Chun, Krystal Benitez, Dhruvil Deven Badani, Yijie Dylan Wang

reinforcement-learning

arXiv AI

Jun 3

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.

By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai

llms reinforcement-learning fine-tuning safety

arXiv AI

22h ago

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

arXiv:2608. 17180v1 Announce Type: cross Abstract: Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related tasks.

By Jianan Zhou, Jung-Hoon Cho, Tianyue Zhou, Han Zheng, Jie Zhang, Roy Dong, Yining Ma, Cathy Wu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 31

Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study

arXiv:2607. 27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.

By Di Bai, Jintao Liu, Zhenwei Tang, Peifan Wu, Nada Al-Thawr, Luoshu Wang

benchmarks safety

arXiv AI

2d ago

EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment

arXiv:2608. 14011v1 Announce Type: cross Abstract: Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the shared token space.

By Haokai Ma, Aoqi Hu, Yueao Xing, Ruobing Xie, Yonghui Yang, Teng Tu, Lei Meng, Tat-Seng Chua

safety

arXiv Machine Learning

Jun 2

PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning

arXiv:2605. 21422v3 Announce Type: replace Abstract: As LLMs continue to scale up, improving training efficiency heavily relies on effective data utilization.

By Qihao Lin, Guanxu Chen, Dongrui Liu, Jing Shao

llms fine-tuning safety

arXiv AI

Jun 9

Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation

arXiv:2606. 08480v1 Announce Type: cross Abstract: Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement.

By Kewei Xu, Junbo Qi, Yanyan Zou, Pengfei Zhang, Xingzhi Yao, Shengjie Li

reinforcement-learning safety

arXiv AI

Jun 2

Collaborative and Efficient Fine-tuning: Leveraging Task Similarity

arXiv:2602. 07218v2 Announce Type: replace-cross Abstract: Adaptability has been regarded as a central feature in the foundation models, enabling them to effectively acclimate to unseen downstream tasks.

By Gagik Magakyan, Amirhossein Reisizadeh, Chanwoo Park, Pablo A. Parrilo, Asuman Ozdaglar

fine-tuning

arXiv AI

1d ago

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

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.

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

reinforcement-learning fine-tuning benchmarks safety

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