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Yi Su

ACL 2026×2NeurIPS 2026推理评测与基准效率与系统可解释性对齐与偏好
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1 一作 · 3 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

3
作者位次 #23/26ACL 2026Poster· Natural Language Processing

O ne R ec-Think: In-Text Reasoning for Generative Recommendation

Zhanyu Liu · Shiyao Wang · Xingmei Wang · Rongzhou Zhang · Jiaxin Deng · Honghui Bao · Jinghao Zhang · Wuchao Li · PengFei Zheng · Xiangyu Wu · Yifei Hu · Qigen Hu · Xinchen Luo · Lejian Ren · Zhang Zixing · Qianqian Wang · Kuo Cai · Yunfan Wu · Hongtao Cheng · Zexuan Cheng · Lu Ren · Huanjie Wang · Yi Su · Ruiming Tang (Huawei Noah&#x27 · Kun Gai · Guorui Zhou

摘要

The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as implicit predictors, critically lacking the capacity for explicit and controllable reasoning—a key advantage of LLMs. T...

对齐与偏好推理评测与基准OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains

Yi Su · Dian Yu · Linfeng Song · Juntao Li · Haitao Mi · Zhaopeng Tu · Min Zhang · Dong Yu

摘要

Reinforcement learning with verifiable rewards (RLVR) has been effective on tasks with structured solutions like math and coding, but its reliance on simple, rule-based verifiers creates a fundamental bottleneck. We find their applicability is surprisingly narrow even in structured domains, a lim...

强化学习与后训练评测与基准推理OpenReview 项目页

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