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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, Google accidentally published a blog post about the Gemini CLI, but the page is now returning a 404 error. The unpublished article revealed several key features of the Gemini CLI, sparking discussions in the developer community. The content indicates that the Gemini CLI is an open-source command-line interface (CLI) tool for Gemini2.5Pro, supporting a context window of up to 1 million tokens, suitable for handling complex code and large-scale data.

The plan's advancement has triggered dissatisfaction among developers. According to VentureBeat, OpenAI's current API deprecation involves the GPT-4.5 Preview version, which is an intermediary model launched between GPT-4 and GPT-5. Multiple developers have stated that this sudden change has disrupted their development plans, forcing significant adjustments to applications already built based on this API. A developer who wished to remain anonymous commented: We understand the necessity of technological iteration, but the lack of sufficient buffer time in the schedule has brought additional pressure to development teams.

Recently, the DeepSeek team released a technical paper about their latest model, DeepSeek-V3, focusing on the challenges of scaling in large AI model training and related considerations of hardware architecture. This 14-page paper not only summarizes the experiences and lessons learned during the development of V3 by DeepSeek but also provides deep insights for future hardware design. Notably, CEO of DeepSeek, Liang Wenfeng, also participated in writing the paper.

A new Google report reveals that effective AI training for employees could unlock a £400 billion (approximately $533 billion) boost to the UK economy from AI-driven growth. Based on a UK pilot program, the report shows employees saved over 122 hours annually on administrative tasks by using AI tools. The report highlights that simplifying AI usage and providing adequate training are key to wider AI adoption. Google's European, Middle...