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2026 顶会论文

作者位次 #2/6ICLR 2026Poster· Reinforcement Learning / Everything Else

When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets

Aladin Djuhera · Farhan Ahmed · Swanand Kadhe · Syed Zawad · Heiko Ludwig · Holger Boche

对齐与偏好强化学习与后训练数据与预训练OpenReview

作者位次 #7/32ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

GneissWeb: Preparing High Quality Data for LLMs at Scale

Hajar Emami Gohari · Swanand Kadhe · Yousaf Shah · Constantin Adam · Abdulhamid Adebayo · Praneet Adusumilli · Farhan Ahmed · Nathalie Baracaldo · Santosh Borse · Yuan-Chi Chang · Xuan-Hong Dang · Nirmit Desai · Revital Eres · Ran Iwamoto · Alexei Karve · Yan Koyfman · Wei-Han Lee · Changchang Liu · Boris Lublinsky · Takuya Ohko · Pablo Pesce · Maroun Touma · Shiqiang Wang · Shalisha Witherspooon · Herbert Woisetschläger · David Wood · Kun-Lung Wu · Issei Yoshida · Syed Zawad · Petros Zerfos · Yi Zhou · Bishwaranjan Bhattacharjee

OpenReview

作者位次 #3/5ACL 2026Findings· Findings of ACL

S afe MERGE : Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

Aladin Djuhera · Swanand Ravindra Kadhe · Farhan Ahmed · Syed Zawad · Holger Boche

摘要

Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts. Many methods to realign safety have been proposed, bu...

安全对齐与偏好强化学习与后训练OpenReview 项目页

Farhan Ahmed

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2026 顶会论文

When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets

GneissWeb: Preparing High Quality Data for LLMs at Scale

S afe MERGE : Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

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