ORCHESTRATING SYNTHETIC DATA WITH REASONING Davidson et al.: https://
openreview.net/pdf?id=VOoeogZ
bMb
… #ArtificialIntelligence #DeepLearning #MachineLearning
LLMS
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Orchestrating Synthetic Data With Reasoning in AI
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Anthropic Gives Claude Autonomous Research Capabilities with Google Workspace
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Anthropic dote Claude de capacités de “Recherche Autonome”. Claude peut désormais explorer Google Workspace, gérer des questions complexes en plusieurs étapes, et fournir des réponses sourcées. Il analyse votre requête sous plusieurs angles, mène ses recherches et livre ses
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ALPHA-FACTORY V1 Sets New AGI Assessment Benchmark
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ALPHA-FACTORY V1 AGI Assessment https://
chatgpt.com/share/680e79bc
-3278-8013-ba10-4c11e625947e
… "ALPHA-FACTORY V1 sets a new benchmark at the edge of current AI knowledge and practice." #AGI #AGIALPHA #ASI -
Subscribe to Weekly AI Engineering Tutorials on ML and LLMs
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If you're interested in ML, LLMs, RAG, and AI Agents and want to receive tutorials every week, subscribe to AI Engineering (for free):
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ChatGPT gains one IQ point weekly: AI explosion implications
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ChatGPT gagne 1 point de Quotient Intellectuel chaque semaine C’est une progression incroyable Cette explosion de l’IA va conduire à quoi ? Ou en sera-t-on en 2050 ?
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Scaling Laws in LLMs: Beyond Classic Models and Inference Costs
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Great set of slides by my Gemini colleague @FeinbergVlad on scaling considerations in large language models, addressing the fact that the classic "scaling law" work does not take into account inference cost (!), distillation, learning rate schedules, etc. https://
vladfeinberg.com/2025/04/24/gem
ini-flash-pretraining.html
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Multi-Modal RAG System with Gemma 3 and LangChain
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Multi-Modal RAG with Gemma 3 Build a powerful RAG system that processes mixed-content PDFs using Google's Gemma 3 and LangChain. This implementation combines PDF processing with multi-modal support, powered by Streamlit and Ollama. Check out the tutorial
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General-Reasoner: Reinforcement Learning Boosts LLM Reasoning
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9. General-Reasoner General-Reasoner is a reinforcement learning approach that boosts LLM reasoning across diverse domains by using a 230K-question dataset and a model-based verifier trained to understand semantics beyond exact matches.
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Tiny Reasoning Models: 1.5B Parameter Tina Family
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10. Tiny Reasoning Models Tina is a family of 1.5B parameter reasoning models trained using LoRA-based reinforcement learning (RL) to achieve high reasoning accuracy at very low cost.
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Assessing LLM Goal-Directedness: New Evaluation Framework
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8. Evaluate the Goal-Directedness of LLMs Introduces a new framework to assess whether LLMs use their capabilities effectively toward achieving given goals.