The recent years have witnessed great advances in video generation. However, the
development of automatic video metrics is lagging significantly behind. None of the existing …

As large language models (LLMs) advance, it becomes more challenging to reliably
evaluate their output due to the high costs of human evaluation. To make progress towards …

In this report, we introduce a collection of methods to enhance reward modeling for LLMs,
focusing specifically on data-centric techniques. We propose effective data selection and …

Traditionally, reward models used for reinforcement learning from human feedback (RLHF)
are trained to directly predict preference scores without leveraging the generation …

This paper surveys research in the rapidly growing field of enhancing large language
models (LLMs) with reinforcement learning (RL), a technique that enables LLMs to improve …

Current evaluations of large language models (LLMs) often overlook non-determinism,
typically focusing on a single output per example. This limits our understanding of LLM …

Reward models (RM) play a critical role in aligning generations of large language models
(LLM) to human expectations. However, prevailing RMs fail to capture the stochasticity …

Foundation Models (FMs) are gaining increasing attention in the biomedical artificial
intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal …

LLMs are typically trained to answer user questions or follow instructions similarly to how
human experts respond. However, in the standard alignment framework they lack the basic …

Reward modeling is crucial for aligning large language models (LLMs) with human
preferences, especially in reinforcement learning from human feedback (RLHF). However …