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

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Microsoft and OpenAI could be renegotiating the partnership

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ChatGPT now allows you to download deep research as PDF

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AI adoption: AWS addresses skills barrier that is holding back enterprises

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Glass Imaging raises $20M for AI to improve digital images quality

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Aarki launches Encore, a privacy-first mobile growth platform.

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Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained...

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A Coding Guide to Unlock mem0 Memory for Anthropic Claude Bot:...

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Tencent Released PrimitiveAnything: A New AI Framework That Reconstructs 3D Shapes...

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A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

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LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for...

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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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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...