Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

More than three billion years of evolution have produced an image of biology encoded into
the space of natural proteins. Here we show that language models trained at scale on …

Recent methodologies in LLM self-training mostly rely on LLM generating responses and
filtering those with correct output answers as training data. This approach often yields a low …

Verifiers or reward models are often used to enhance the reasoning performance of large
language models (LLMs). A common approach is the Best-of-N method, where N candidate …

Training on high-quality synthetic data from strong language models (LMs) is a common
strategy to improve the reasoning performance of LMs. In this work, we revisit whether this …

This paper introduces a pioneering approach to artificial intelligence research, embodied in
our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking …

Recent studies have shown that large language models'(LLMs) mathematical problem-
solving capabilities can be enhanced by integrating external tools, such as code …

Existing open-source multimodal large language models (MLLMs) generally follow a
training process involving pre-training and supervised fine-tuning. However, these models …

When large language models (LLMs) exceed human-level capabilities, it becomes
increasingly challenging to provide full-scale and accurate supervision for these models …

Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving
interpretability and trustworthiness. However, current training recipes lack robust CoT …