In this tutorial, we'll learn how to create a custom tokenizer using the tiktoken library. The process involves loading a pre-trained tokenizer model, defining both base and special tokens, ...

Reasoning tasks are yet a big challenge for most of the language models. Instilling a reasoning aptitude in models, particularly for programming and mathematical applications that require solid ...

The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable ...

Competitive programming has long served as a benchmark for assessing problem-solving and coding skills. These challenges require advanced computational thinking, efficient algorithms, and precise ...

Multi-agent AI systems utilizing LLMs are increasingly adept at tackling complex tasks across various domains. These systems comprise specialized agents that collaborate, leveraging their unique ...

Artificial intelligence models face a fundamental challenge in efficiently scaling their reasoning capabilities at test time. While increasing model size often leads to performance gains, it also ...

Large Language Models (LLMs) have revolutionized natural language processing (NLP) but face significant challenges in practical applications due to their large computational demands. While scaling ...

Artificial Intelligence is increasingly integrated into various sectors, yet there is limited empirical evidence on its real-world application across industries. Traditional research methods—such as ...

Machines learn to connect images and text by training on large datasets, where more data helps models recognize patterns and improve accuracy. Vision-language models (VLMs) rely on these datasets to ...

LLMs have demonstrated exceptional capabilities, but their substantial computational demands pose significant challenges for large-scale deployment. While previous studies indicate that intermediate ...

Human-robot collaboration focuses on developing intelligent systems working alongside humans in dynamic environments. Researchers aim to build robots capable of understanding and executing natural ...

Recent advancements in LLMs, such as the GPT series and emerging “o1” models, highlight the benefits of scaling training and inference-time computing. While scaling during training—by increasing model ...