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Large Language Models (LLMs) have achieved significant progress in complex reasoning tasks by combining task prompts with large-scale reinforcement learning (RL), as demonstrated by models like Deepseek-R1-Zero, which directly apply reinforcement learning to base models, showcasing strong reasoning capabilities. However, this success is difficult to replicate across different base model families, especially within the Llama series. This raises a core question: what factors lead to inconsistent performance of different base models during reinforcement learning? How does reinforcement learning perform in

Recently, Meta Platforms is facing a major decision, possibly abandoning its self-developed Llama AI model and adopting artificial intelligence systems from competitors such as OpenAI and Anthropic. This change reflects a significant adjustment in Meta's open-source AI strategy and also shows the company's dissatisfaction with its own product performance. The turning point occurred at the Llama 4 launch event in April, where the product was presented at Meta's Llama

A recent paper published by researchers from Stanford University, Cornell University, and West Virginia University shows that Meta's Llama3.1 AI model can reproduce large amounts of copyrighted book content word for word, posing potential significant legal risks for the tech giant. The study indicates that the Llama3.170B model was able to reproduce 42% of the text from 'Harry Potter and the Philosopher's Stone' in testing, far exceeding the 4.4% reproduced by the first-generation Llama model. Artificial intelligence models, such as Ope

Recently, a move by Meta founder Mark Zuckerberg has drawn significant attention. Disappointed with the performance of the company's latest open-source large model, Llama4, Zuckerberg has decided to personally form a brand-new 'Superintelligence Team' in an effort to regain competitive advantage in the field of artificial intelligence. The goal of this new team is to achieve general artificial intelligence (AGI), making machines reach human-level performance across multiple tasks. According to insider sources, Zuckerberg has prioritized the formation of the new team and has already established a group called

NVIDIA officially released Llama Nemotron Nano VL on June 3, 2025. This compact visual-language model (VLM) is specially optimized for document intelligence processing. The model topped the OCRBench v2 benchmark test, showcasing its outstanding capabilities in handling complex documents, charts, and video frames. With efficient inference performance and flexible deployment options, Llama Nemotron Nano VL provides enterprises with solutions from the cloud.

Meta announced a major reorganization of its AI division, splitting it into two core teams: 'AI Products' and 'AGI Foundations'. This move aims to address technical challenges and accelerate product development. In this restructuring, the original AI division has been divided into two key teams: the 'AI Products' team, led by Connor Hayes, focuses on the Meta AI Assistant and its functional applications on platforms such as Facebook, Instagram, and WhatsApp. The 'AGI Foundations' team is led by Ahm

Meta's open-source large language model, Llama-4-Maverick, has experienced a dramatic drop in LMArena rankings, plummeting from second place to 32nd. This significant shift has sparked widespread skepticism among developers, who suspect Meta may have manipulated the benchmark by submitting a specially optimized version. The issue stems from Meta's April 6th release of its latest large language model, Llama 4, encompassing three versions: Scout, Maverick, and Behemoth.

Recently, the AI code generation field has seen a surge in open-source releases, with several heavyweight models making their debut. Among them, the Cogito v1 Preview series from Deep Cogito stands out. According to AIbase, this new family of open-source models includes various sizes: 3B, 8B, 14B, 32B, and 70B parameters. Not only does it outperform competitors in its class, but its 70B version even surpasses Meta's recently released Llama 4 109B MoE model, sparking considerable industry discussion.

Google Cloud Platform recently announced that Meta's latest generation of open-source large language models, Llama 4, is now available in its Vertex AI Model Garden. The news has generated significant excitement in the global tech community. The Scout and Maverick models from the Llama 4 series are now integrated into Vertex AI and available to developers via fully managed Model-as-a-Service (MaaS) API endpoints in preview.

NVIDIA, a global leader in chip and AI technology, recently launched a groundbreaking new open-source large language model, Llama 3.1 Nemotron Ultra 253B, generating significant excitement within the AI community. Built upon Meta's Llama-3.1-405B, this model boasts innovative optimizations that surpass competitors like Llama 4 Behemoth and Maverick in performance, while demonstrating superior resource efficiency and exceptional multi-tasking capabilities.

On April 8th, 2025, NVIDIA launched Llama 3.1 Nemotron Ultra 253B, an open-source model optimized from Llama-3.1-405B. With 25.3 billion parameters, it surpasses Meta's Llama 4 Behemoth and Maverick, becoming a focal point in the AI field. This model demonstrates superior performance in benchmarks such as GPQA-Diamond, AIME 2024/25, and LiveCodeBench, achieving inference throughput comparable to DeepSeek.

Meta has released its latest open-source AI model, Llama 4, marking another significant advancement in the field of artificial intelligence. Llama 4 comes in two versions, Scout and Maverick, designed to enhance AI model capabilities and performance. Meta states that Llama 4 is a multimodal large language model capable of processing various data types, including text, images, video, and audio, and can freely convert between these formats. Notably, the Llama 4 series is the first...