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

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers...

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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That...

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Google Launches Gemini 2.5 Pro I/O: Outperforms GPT-4 in Coding, Supports...

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Hugging Face Releases nanoVLM: A Pure PyTorch Library to Train a...

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NVIDIA Open-Sources Open Code Reasoning Models (32B, 14B, 7B)

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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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Duolingo shifts to AI-first model, cutting contractor roles

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UK opens Europe’s first E-Beam semiconductor chip lab

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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...