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Offline Video-LLMs Can Now Understand Real-Time Streams: Apple Researchers Introduce StreamBridge...

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Gemini 2.5: Our most intelligent AI model

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Evaluating potential cybersecurity threats of advanced AI

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Taking a responsible path to AGI

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DolphinGemma: How Google AI is helping decode dolphin communication

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Generate videos in Gemini and Whisk with Veo 2

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Introducing Gemini 2.5 Flash

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Music AI Sandbox, now with new features and broader access

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Build rich, interactive web apps with an updated Gemini 2.5 Pro

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Gemini 2.5 Pro Preview: even better coding performance

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The genius of SUPER drone’s two-trajectory strategy

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RL^V: Unifying Reasoning and Verification in Language Models through Value-Free Reinforcement...

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Implementing an LLM Agent with Tool Access Using MCP-Use

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A Step-by-Step Guide to Deploy a Fully Integrated Firecrawl-Powered MCP Server...

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Education

Reinforcement Learning, Not Fine-Tuning: Nemotron-Tool-N1 Trains LLMs to Use Tools with...

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RL^V: Unifying Reasoning and Verification in Language Models through Value-Free Reinforcement...

LLMs have gained outstanding reasoning capabilities through reinforcement learning (RL) on correctness rewards. Modern RL algorithms for LLMs, including GRPO, VinePPO, and Leave-one-out PPO, have moved away from traditional PPO approaches by eliminating the learned value function network in favor of empirically estimated returns. This reduces computational demands and...