The rapid advancement of language models (LMs) necessitates robust alignment with
diverse user values. However, current preference optimization approaches often fail to …

State-of-the-art multimodal web agents, powered by Multimodal Large Language Models
(MLLMs), can autonomously execute many web tasks by processing user instructions and …

As large language models (LLMs) advance, their potential applications have grown
significantly. However, it remains difficult to evaluate LLM behavior on user-specific tasks …

Reinforcement learning from human feedback (RLHF) methods are emerging as a way to
fine-tune diffusion models (DMs) for visual generation. However, commonly used on-policy …

Controlling the format of outputs generated by large language models (LLMs) is a critical
functionality in various applications. Current methods typically employ constrained decoding …

Language models are aligned to the collective voice of many, resulting in generic outputs
that do not align with specific users' styles. In this work, we present Trial-Error-Explain In …

Aligning large language models (LLMs) to human preferences is challenging in domains
where preference data is unavailable. We address the problem of learning reward models …

Preferences within a group of people are not uniform but follow a distribution. While existing
alignment methods like Direct Preference Optimization (DPO) attempt to steer models to …

Large Language Models (LLMs) are often characterized as samplers or generators in the
literature, yet maximizing their capabilities in these roles is a complex challenge. Previous …

Many harmful behaviors and problematic deployments of AI stem from the fact that AI experts
are not experts in the vast array of settings where AI is applied. Non-AI experts from these …