谷歌用8B小模型暴打Gemini pro ! 神论揭秘大模型真正的“长期记忆”

👉 Hostinger Exclusive Purchase Link: https://hostinger.com/WOWINSIGHT 🔥 Enter the exclusive discount code: WOWINSIGHT at checkout to enjoy an extra 10% discount! Is your AI always "forgetting" and repeatedly falling into the same trap? Stop stuffing history into your Prompt or relying on traditional RAG (Retrieval Augmentation Generation)! In this video, I'll break down the top-tier conference paper "Skill.OS" to show you how an open-source "small model" with only 8 parameters completely separates "execution" and "experience management," utterly outperforming Google's flagship Gemini 2.5 Pro. It not only gives large models true "procedural long-term memory" and enables them to "self-evolve," but also helps you significantly save on API costs! Tired of your AI agents having "amnesia" and making the same mistakes over and over? Stop stuffing chat history into your prompts or relying solely on traditional RAG! In this video, we dive deep into the groundbreaking paper "Skill.OS." Discover how a tiny 8B parameter open-source model outperforms Google's Gemini 2.5 Pro by separating task execution from experience curation. Learn how to give your LLMs true "long-term procedural memory," enabling them to self-evolve while saving you a fortune on API costs! ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 📄 Key Content & Keywords: Procedural Memory vs. Factual Memory: Why traditional RAG cannot solve Agent What are the pain points in the workflow? We delve into the fundamental difference between factual knowledge retrieval and procedural methodology. Why traditional RAGs fail to solve Agent workflow issues. We explore the fundamental difference between factual knowledge retrieval and procedural methodology. Skill Curator and System Decoupling: Unveiling the core philosophy of Skill.OS—completely separating the frozen execution model from the trainable curation model, allowing an 8B open-source model to outperform large proprietary models. White-box Experience Management (White-box Memory & Markdown Repo): Say goodbye to incomprehensible black-box vector libraries (Vector DBs) and see how plain text Markdown brings high interpretability and human intervention capabilities to AI experience. Say goodbye to black-box vector databases. See how managing AI experiences in pure text Markdown brings high interpretability and allows for direct human intervention. GRPO and Combinatorial Reward Optimization (RL & Reward Optimization): A hardcore analysis of how "grouped task flow" and four dimensions of KPIs (including compression refinement and expert scoring) can solve the problem of delayed feedback in reinforcement learning, forcing AI to extract "meta-skills." A hardcore breakdown of how "Grouped Task Streams" and a 4-dimensional KPI reward system solve delayed feedback in RL, forcing AI to extract universal "meta-skills." ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🔔 Subscribe & Join my membership! In your daily operations, how do you handle the "memory and experience" of your AI agent? Do you use a Vector DB or rely on stacked prompts? Feel free to share your tips in the comments! How do you handle the "memory and experience" of AI Agents in your daily projects? Vector DBs, prompt engineering, or something else? Share your thoughts in the comments below! If you enjoyed this video, please like, share, and subscribe to my channel to get notified of in-depth analysis of cutting-edge technologies and large-scale model development. 👉 Support My Work: Join my channel membership to get early access to videos and exclusive benefits! Perks!    / @wow.insight   ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Paper link, please click on the member post:    • Post   ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #AIAgent #SkillOS #LLM #RAG #Gemini #PromptEngineering #MachineLearning #ReinforcementLearning #ArtificialIntelligence #AIAgent #LargeModel #VectorDatabase #Developers #SelfEvolution #HardcoreSciencePopularization