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20242026
most citedCal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2026

StepAudio 2.5 Technical Report

Bin Lin, Bo Zhao, Boyong Wu +98

Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…

cs.LG2025

On a Connection Between Imitation Learning and RLHF

Teng Xiao, Yige Yuan, Mingxiao Li +2

This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcem…

cs.LG2025

SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters

Teng Xiao, Yige Yuan, Zhengyu Chen +4

Existing preference optimization objectives for language model alignment require additional hyperparameters that must be extensively tuned to achieve optimal performance, increasin…

cs.LG2024★ 1 cited

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment

Teng Xiao, Yige Yuan, Huaisheng Zhu +2

We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with avai…

cs.CL2024

How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective

Teng Xiao, Mingxiao Li, Yige Yuan +3

This paper introduces a novel generalized self-imitation learning () framework, which effectively and efficiently aligns large language models with offline demonstra…