Yaodong Yang

PAC Learnability of Approximate Nash Equilibrium in Bimatrix Games

2021 · arXiv (Cornell University)

Computing Nash equilibrium in bimatrix games is PPAD-hard, and many works have focused on the approximate solutions. When games are generated from a fixed unknown distribution, learning a Nash predictor via data-driven approaches can be …

Settling the Communication Complexity for Distributed Offline Reinforcement Learning

2022 · arXiv (Cornell University)

We study a novel setting in offline reinforcement learning (RL) where a number of distributed machines jointly cooperate to solve the problem but only one single round of communication is allowed and there is a …

BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset

2023 · arXiv (Cornell University)

In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs). This dataset uniquely separates annotations of helpfulness and harmlessness for question-answering pairs, thus offering distinct …

Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models

2024 · arXiv (Cornell University)

Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing. Ideally, by training on decentralized data that is aligned with human preferences and safety …

SafeLawBench: Towards Safe Alignment of Large Language Models

2025 · arXiv (Cornell University)

With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluating their safety due to the subjective nature …