The key idea is to reframe RL as a sequence modeling problem, allowing the use of powerful transformer architectures and language modeling advances. …
The realm of artificial intelligence has witnessed a significant breakthrough with the introduction of the SELF-DISCOVER framework, a novel approach that empowers Large Language Models (LLMs) to autonomously uncover and employ intrinsic reasoning structures. This advancement is poised to redefine how AI systems tackle complex reasoning challenges, offering a more efficient and interpretable method compared to traditional prompting techniques. …
In the ever-evolving landscape of artificial intelligence, a groundbreaking development emerges with "Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution." This paper introduces an innovative approach that pushes the boundaries of how Large Language Models (LLMs) can be enhanced, not through manual tweaks but via an evolutionary mechanism that refines the art of prompting itself. …
Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …
The paper "A Decoder-Only Foundation Model for Time-Series Forecasting" introduces a groundbreaking approach in the field of time-series forecasting, leveraging the power of decoder-only models, commonly used in natural language processing, to achieve remarkable zero-shot forecasting capabilities across a variety of domains. …