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Reinforcement Learning Teachers of Test Time Scaling

Reinforcement-Learned Teachers (RLTs)ripped through LLM training bloat by swapping "solve everything from ground zero" with "lay it out in clear terms." Shockingly, a lean 7B model took down hefty beasts likeDeepSeek R1. These RLTs flipped the script, letting smaller models school the big kahunas wi.. read more  

Reinforcement Learning Teachers of Test Time Scaling
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@faun shared a link, 11 months, 1 week ago
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Deploying Llama4 and DeepSeek on AI Hypercomputer

Meta's Llama4models, Scout and Maverick, strut around with17B active parametersunder a Mixture of Experts architecture. But deploying onGoogle Cloud's Trillium TPUsor A3 GPUs? That's become a breeze with new, fine-tuned recipes. Utilizing tools likeJetStreamandPathways? It means zipping through infe.. read more  

Deploying Llama4 and DeepSeek on AI Hypercomputer
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Lenovo introduces new AI-optimized data center systems

Lenovo'sThinkSystem SR680a V4doesn't just perform—it explodes with AI power, thanks to Nvidia'sB200GPUs. We're talking4nmchips with a mind-boggling208 billion transistors. Boost? Try11x... read more  

Lenovo introduces new AI-optimized data center systems
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ChatGPT polluted the world forever, like the first atom bomb

AI model collapsecould hit hard with synthetic data in play. Picturepre-2022 dataas the “low-background steel” savior for pristine datasets. The industry squabbles over thetrue fallout, while researchers clamor for policies that keep data unsullied. The worry? AI behemoths might lock everyone else o.. read more  

ChatGPT polluted the world forever, like the first atom bomb
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Amazon CEO warns staff: Eat or be eaten by AI

Amazon'sCEO sounds the alarm: AI is gearing up to decimate office jobs. He urges employees to sharpen their skills or risk getting the axe, all while Amazon unleashes a cavalcade of over1,000generative AI projects... read more  

Amazon CEO warns staff: Eat or be eaten by AI
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A Reality Check on DeepSeek's Distributed File System Benchmarks

3FSisn't quite matching its own hype. Yes, it boasts a flashy8 TB/s peak throughput, but pesky network bottlenecks throttle usage to roughly 73% of its theoretical greatness. Efficiency’s hiding somewhere, laughing. A dig intoGraySortshows storage sulking on the sidelines, perhaps tripped up by CRAQ.. read more  

A Reality Check on DeepSeek's Distributed File System Benchmarks
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Why AI Features Break Microservices Testing and How To Fix It

GenAIcomplexity confounds conventional testing. But savvy teams? They fast-track validation insandbox environments, slashing AI debug time from weeks down to mere hours... read more  

Why AI Features Break Microservices Testing and How To Fix It
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Run the Full DeepSeek-R1-0528 Model Locally

DeepSeek-R1-0528's nanized form chops space needs down to162GB. But here's the kicker—without a solid GPU, it's like waiting for paint to dry... read more  

Run the Full DeepSeek-R1-0528 Model Locally
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Training a Rust 1.5B Coder LM with Reinforcement Learning (GRPO)

DeepSeek-R1flips the script on training LLMs. Armed withGRPO, it challenges the industry heavies like OpenAI's o1 by playing smart with custom data and cleverly designed rewards. Imagine this: a humble 1.5B model, running on merely asingle H100, clocks in at an 80% build pass rate. It’s nibbling at .. read more  

Training a Rust 1.5B Coder LM with Reinforcement Learning (GRPO)
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Mistral named most privacy-friendly AI, Google ranks low: report

Mistral AI’s “Le Chat” leads in privacy-focused AI, beating out OpenAI’s ChatGPT and xAI’s Grok.Consumer privacy concerns are reshaping the AI landscape, with 68% worried about online privacy.Regional regulations impact privacy practices, with Mistral AI benefiting from Europe’s strict GDPR rules... read more  

Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.