paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
PF

Peinan Feng

ACL 2026×2强化学习与后训练多语言与语言学评测与基准推理可解释性
新星分贡献者
41
一作贡献0
核心/参与10
方向广度10
合作网络9
0 一作 · 2 篇 · ACL 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
作者位次 #9/12ACL 2026Poster· Natural Language Processing

N iu T rans. LMT : Toward Inclusive and Scalable Multilingual Machine Translation with LLM s

Yingfeng Luo · Ziqiang Xu · Yuxuan Ouyang · MuRun Yang · DingYang Lin · Kaiyan Chang · Tong Zheng · Bei Li · Peinan Feng · Quan Du · Tong Xiao · JingBo Zhu

摘要

Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging.In this paper, we identify a failure mode of multilingual supervised fine-tuning (SFT) on multi-way parallel d...

多语言与语言学强化学习与后训练评测与基准OpenReview 项目页
作者位次 #5/12ACL 2026Findings· Findings of ACL

SPS : Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models

Yifu Huo · Chenglong Wang · Ziming Zhu · Shunjie Xing · Peinan Feng · Tongran Liu · Qiaozhi He · Tian Hua Zhou · Changxiaojia · JingBo Zhu · Zhengtao Yu · Tong Xiao

摘要

Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends to improve single-sample success rates (i.e., Pass@1) while offering limited exploration of diverse reasoning trajec...

强化学习与后训练推理可解释性OpenReview 项目页

常见合作者