About 49,000 migrants enter Spanish enclave of Ceuta, officials say https://t.co/uniOxv8Zzi ...

From Gaza to Kyiv, civilians describe how drones are transforming not just the battlefield but the sounds and psychology of modern conflict. https://t.co/bEePbwLEPT ...

Falcon 9 successfully launches the **NROL-95** mission to orbit from **Space Launch Complex 40** in Florida. 🚀🇺🇸• Liftoff from Cape Canaveral, Florida.• Mission: **NROL-95**.• Launched aboard a SpaceX Falcon 9 for the U.S. National Reconnaissance Office (NRO).Another successful Falcon 9 mission is underway. ...

“Some people don’t like change, but you need to embrace change if the alternative is disaster.”― Elon Musk https://t.co/9P83DfYgG8 ...

RT BBC Breaking News (@BBCBreaking)Asian nations join growing opposition to Fifa's controversial investment plan https://t.co/obIqtTJSrU ...

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Six Saudi oil tankers are sailing away from the Bab el-Mandeb chokepoint and appeared to be embarking on an unusual trip around Africa https://t.co/5Y4WKcYEcf ...

Training Stages and Architecture - Model weights are initialized randomly during the initial phase, causing token selection to remain near uniform and the large language model to generate meaningless text[1] - The pre-training stage consumes nearly all training computational resources, allowing the model to process large text corpora and acquire grammar, world facts, and raw reasoning abilities[2] Fine-Tuning and Optimization - Instruction fine-tuning utilizes pairs of instructions and high-quality responses to help the model adopt the convention of mapping questions to answers and requests to summaries[2] - OpenAI utilized roughly 13,000 human-written examples for GPT-3, trained against a pre-training corpus consisting of hundreds of billions of words[3] - Preference fine-tuning employs human evaluation where two responses are reviewed and the preferred choice trains a separate model to predict human selection, enabling the main model to score higher[4] - Reasoning fine-tuning applies program-based verification for mathematics and code where a single correct answer provides an automated reward signal without requiring human preference[4][5] - DeepSeek trained the R1 model using Group Relative Policy Optimization (GRPO), enabling emergent behaviors such as intermediate reasoning steps and self-correction without demonstration examples[7]

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