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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, ERGO Innovation Lab and ECODYNAMICS conducted a study focusing on how insurance-related content is displayed in AI-driven search. The research analyzed over 33,000 AI search results and 600 websites, exploring the preferences of large language models (LLMs) such as ChatGPT when processing this content. The study found that LLMs tend to prioritize content that is easy to read, well-structured, and trustworthy, which closely aligns with traditional SEO strategies.

OpenAI announced the official opening of API access to its deep research models, providing developers with a powerful set of tools including automatic web search, data analysis, MCP (Model Communication Protocol), and code execution. The models opened include the deep research versions of o3 and o4-mini, which have already been used in ChatGPT. Now, developers can directly call these models via API. These models are particularly suitable for complex tasks that require obtaining the latest information and performing advanced reasoning. In terms of function expansion, o3,

Google DeepMind recently published a blog post, officially introducing the new Gemini Robotics On-Device localized robot AI model. The model uses a vision-language-action (VLA) architecture, enabling precise control of physical robots without cloud support. The biggest highlight of the new model is that it runs entirely locally on the robot device, achieving low-latency response capabilities. This feature makes it particularly suitable for environments with unstable network connections, such as critical application scenarios like medical facilities.

On June 24, Faraday Future officially announced the launch of the FF AI2.0 intelligent cockpit operating system, which is the most significant cockpit upgrade since the release of the FF91 in 2023. The new system will be deployed to the FF91 2.0 model first through OTA software updates, and it is planned to be extended to the FX series in the future. FF AI2.0 features a complete restructuring of the entire AI architecture, with its biggest highlight being the deep integration with OpenAI. The system deeply integrates large language models (LLMs) into Faraday Future's software and hardware architecture.