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In recent years, researchers in the field of robotic reinforcement learning (RL) have achieved significant progress, developing methods capable of handling complex image observations, training in real-world scenarios, and incorporating auxiliary data, such as demonstrations and prior experience. Despite these advancements, practitioners acknowledge the inherent difficulty in effectively utilizing robotic RL, emphasizing that the specific…

In the dynamic arena of artificial intelligence, the intersection of visual and linguistic data through large vision-language models (LVLMs) is a pivotal development. LVLMs have revolutionized how machines interpret and understand the world, mirroring human-like perception. Their applications span a vast array of fields, including but not limited to sophisticated image recognition systems, advanced natural…

Exploring the fusion of artificial intelligence with mathematical reasoning reveals a dynamic intersection where technology meets one of humanity’s oldest intellectual pursuits. The quest to imbue machines capable of parsing and solving mathematical problems stretches beyond mere computation, delving into the essence of cognitive understanding and logical deduction. This journey is marked by the deployment…

With the rising complexity and capability of Artificial Intelligence (AI), its latest innovation, i.e., the Large Language Models (LLMs), has demonstrated great advances in tasks, including text generation, language translation, text summarization, and code completion. The most sophisticated and powerful models are frequently private, limiting access to the essential elements of their training procedures, including…

Large language models (LLMs) have profoundly transformed the landscape of artificial intelligence (AI) in natural language processing (NLP). These models can understand and generate human-like text, representing a pinnacle of current AI research. Yet, the computational intensity required for their operation, particularly during inference, presents a formidable challenge. This issue is exacerbated as models grow…

The emergence of Large Language Models (LLMs) has transformed the landscape of natural language processing (NLP). The introduction of the transformer architecture marked a pivotal moment, ushering in a new era in NLP. While a universal definition for LLMs is lacking, they are generally understood as versatile machine learning models adept at simultaneously handling various…

Self-supervised learning (SSL) has proven to be an indispensable technique in AI, particularly in pretraining representations on vast, unlabeled datasets. This significantly reduces the dependency on labeled data, often a major bottleneck in machine learning. Despite the merits, a major challenge in SSL, particularly in Joint Embedding (JE) architectures, is evaluating the quality of learned…

Generative AI has recently seen a boom, with large language models (LLMs) showing broad applicability across many fields. These models have improved the performance of numerous tools, including those that facilitate interactions based on searches, program synthesis, chat, and many more. Also, language-based methods have made it easier to link many modalities, which has led…

Processing extensive sequences of linguistic data has been a significant hurdle, with traditional transformer models often buckling under the weight of computational and memory demands. This limitation is primarily due to the quadratic complexity of the attention mechanisms these models rely on, which scales poorly as sequence length increases. The introduction of State Space Models…

In an era where language models (LMs) predominantly cater to English, a revolutionary stride has been made with the introduction of CroissantLLM. This model bridges the linguistic divide by offering robust bilingual capabilities in both English and French. This development marks a significant departure from conventional models, often biased towards English, limiting their applicability in…