China, Home to 50% of the World’s AI Researchers, Quietly Launches Upgraded DeepSeek R1 to Challenge OpenAI! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux
LLMS
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Machine Translation with Neural Networks: Deep Learning Approach
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The Machine Translation with Neural Networks. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/MT-Neural-Nets -
Preference: Actions Over Words in AI Agents
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I would not use the term "preference" to describe what an agent *says* is better, only what an agent *does*. It's fragile enough with humans (hence "revealed preference") but with LLMs any relationship between the two must be established from scratch.
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Self-Adapting Language Models Research Breakthrough
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Self-Adapting Language Models Zweiger et al.: https://
arxiv.org/abs/2506.10943 #ArtificialIntelligence #DeepLearning #MachineLearning -
LLM Preferences: Gap Between Talk and Actions
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What an LLM *talks about* in the way of quoted preferences is not even prima facie a sign of preference. What an LLM *does* may be a sign of preference. Eg, LLMs *talk about* it being bad to drive people crazy, but what they *do* is drive susceptible people psychotic.
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Testing LLM Preferences Through Action Rather Than Direct Questions
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To find out if an LLM prefers conversation with crazier people, don't ask it to emit text about whether it prefers conversation with crazier people, give it a chance to feed or refute someone's delusions.
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LlamaRL: Distributed Async RL Framework for Large LLM Training
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9. LLamaRL LlamaRL is a fully-distributed, asynchronous reinforcement learning framework designed for efficient large-scale LLM training (8B to 405B+ models).
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Mistral Launches Magistral: Reasoning-Focused LLM with Custom RL Stack
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7. Magistral Mistral introduces Magistral, its first reasoning-focused LLM line, alongside a custom RL training stack that enables pure reinforcement learning from scratch.
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Code Researcher: Deep Bug Resolution Agent for Complex Systems
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8. Code Researcher Code Researcher is a deep research agent designed to resolve complex bugs in large systems’ code by leveraging multi-step reasoning over semantics, patterns, and commit history.
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Self-Adapting Language Models Through Reinforcement Learning
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5. Self-Adapting Language Models It proposes a novel framework that enables LLMs to adapt themselves through reinforcement learning by generating their own fine-tuning data and update directives, referred to as “self-edits.”