[{"data":1,"prerenderedAt":376},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fOQVZXQlbCjTWLg5ibLu56z7e3Jux1Erv64M5qbTBHbU":245,"article-626":375},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? Would turning them off be murder? No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly writes",{"id":80,"publish_date":81,"is_original":23,"collection":5,"cover_url":82,"cover_url_1_1":83,"title":84,"summary":85,"author":28},309,"2024-03-26","article_res/cover/9877f95894ee88532d0e6012c23a2df3.jpeg","article_res/cover/20092164ddc109ce6ae56b1984246751.jpeg","Learning the Cancun Upgrade with lepton and perplexity","Building a quick conversation-based search demo with Lepton AI.",[87,95,103,111,119],{"id":88,"publish_date":89,"is_original":23,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":28},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models",{"id":144,"publish_date":145,"is_original":23,"collection":146,"cover_url":147,"cover_url_1_1":148,"title":149,"summary":150,"author":28},131,"2024-11-10","#OpenAI","article_res/cover/87f8ed353ce39f31960e7cdfaf075a35.jpeg","article_res/cover/f597a63935f5cd32e484b4aadd6019e8.jpeg","ChatGPT has launched the Search function","Get fast, timely answers with links to relevant web sources.",{"big":152,"small":214},[153,181],{"title":154,"list":155},"AGENT",[156,157,165,173],{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":158,"publish_date":159,"is_original":23,"collection":160,"cover_url":161,"cover_url_1_1":162,"title":163,"summary":164,"author":28},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"id":174,"publish_date":175,"is_original":23,"collection":176,"cover_url":177,"cover_url_1_1":178,"title":179,"summary":180,"author":28},616,"2025-03-29","#MAS #AI Agent #AI Coder #MetaGPT #MGX","article_res/cover/9dcd702ad2035902e5e77967c34a1f1e.jpeg","article_res/cover/0a97fc4a922753c8f46ff38792020df8.jpeg","MGX - An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":96,"publish_date":97,"is_original":23,"collection":98,"articles_id":247,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28,"content":248,"popular":249,"list":317,"category":373,"tag":374},"AZIDBK9mFe7BHWXCJZyGlg","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">In the research of artificial intelligence, reinforcement learning has long been proven as a very powerful learning method in multiple fields. Especially in the field of Go, the AlphaGo system developed by DeepMind is a famous example. This system not only successfully defeated Lee Sedol, one of the top players in the world of Go, but also demonstrated the potential of reinforcement learning in solving complex problems.\u003C/span>\u003C/p>\u003Ch4 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">The Initial Exploration of AlphaGo and Reinforcement Learning\u003C/span>\u003C/span>\u003C/h4>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">AlphaGo's training approach differs from traditional supervised learning models. In supervised learning, the model learns Go techniques by imitating a large number of games played by expert players. Although this method can help improve the model’s performance, its ultimate ability remains limited by human capabilities. Even the best players struggle to break through this bottleneck.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">In reinforcement learning, the model does not simply mimic human players but instead improves by playing against itself, repeatedly trying different moves and using statistical analysis to find the optimal strategies for winning. This learning method is not constrained by human cognition, allowing it to discover strategies that traditional players might overlook, even surpassing the level of top players.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.3356643356643357\" data-s=\"300,640\" data-type=\"png\" data-w=\"1001\" type=\"block\" data-imgfileid=\"100010424\" style=\"height: auto !important;\" src=\"./assets/17433488133280.9974179499643003.png\">\u003C/section>\u003Ch4 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Move 37: A Brilliant Move Unimaginable to Humans\u003C/span>\u003C/span>\u003C/h4>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The reinforcement learning process of AlphaGo has many surprising discoveries. The most famous one is \"Move 37\" —— a rare move made by AlphaGo during its match against Lee Sedol. According to analysis, the probability of making this move was extremely low, almost negligible, and under normal circumstances, no human player would have chosen it. However, upon reviewing the game, this move turned out to be an exceptionally brilliant strategy.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.4638888888888889\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" data-imgfileid=\"100010427\" style=\"height: auto !important;\" src=\"./assets/17433488134700.33767710384671146.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">This case fully demonstrates the potential of reinforcement learning: AlphaGo discovered strategies unforeseen by humans through continuous self-play, achieving incredible success.\u003C/span>\u003C/p>\u003Ch4 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Breakthroughs in Reinforcement Learning and Reasoning Ability\u003C/span>\u003C/span>\u003C/h4>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The power of reinforcement learning is not only reflected in its ability to surpass human levels in Go but also in its capacity to provide new ideas for solving more complex problems. We are gradually applying this learning method to large language models (LLMs) to break through traditional reasoning problem-solving methods.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Unlike Go, the application domain of language models is much broader. It must not only handle structured tasks but also possess more complex reasoning abilities. By setting diverse practice questions and problem environments, the model can continuously refine its thinking patterns across different fields, and may even create new ways of thinking that humans have yet to imagine.\u003C/span>\u003C/p>\u003Ch4 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 18px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Beyond the Boundaries of Language: New Thinking and Language\u003C/span>\u003C/span>\u003C/h4>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The breakthrough of reinforcement learning in reasoning may go beyond the familiar framework of language. In the future, a completely new language might emerge, enabling more efficient thinking and reasoning, potentially surpassing the limitations of English or any existing language. Models could develop their own \"language for reasoning\" according to need, further enhancing their thinking ability.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">This is precisely the frontier of current large language model research. Scientists are creating richer and more diverse \"practice questions\" to provide multi-domain thinking challenges for systems, helping them grow continuously in open-minded environments.\u003C/span>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[250,258,267,276,284,293,300,308],{"id":251,"title_md5":252,"publish_date":253,"author_md5":254,"is_original":4,"collection":5,"summary_md5":255,"cover_url":256,"cover_url_1_1":257},578,"ad52ed8860b6814647b3520f6154852d","2022-04-08","46d4befbaca33274c83a26b5fc7c9d12","2a768ee18896acd2e472b76a5475b0c0","article_res/cover/6a7491f3589cea5605d8636fc3af76e3.jpeg","article_res/cover/379c431a0395611b8e27940c85d29b90.jpeg",{"id":259,"title_md5":260,"publish_date":261,"author_md5":262,"is_original":4,"collection":263,"summary_md5":264,"cover_url":265,"cover_url_1_1":266},473,"3189486fc6f498f1b5504f379191b9ac","2023-04-21","bc27fa490c4d0d525bac812fc0793534","#Prompt Engineering 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#LoRA","ec689c8e35f54bac86a6ff600c7962c7","article_res/cover/9e1700f6770672bbbcb97068b210e60e.jpeg","article_res/cover/472440aa68b9e44596e20e30df662484.jpeg",{"id":285,"title_md5":286,"publish_date":287,"author_md5":288,"is_original":4,"collection":289,"summary_md5":290,"cover_url":291,"cover_url_1_1":292},54,"217ac263b821191df70c913e7d74d94e","2025-01-30","8b408b85a59a084e65ee0ab1f8b139e0","#Leonis Capital #AI Trend #AI Prediction #The State of AI","43bbffd6b667317f41051c2826f96cef","article_res/cover/fa96fe293e6dede46027b06e31d24f13.jpeg","article_res/cover/c756c97a9143c733481a66b2281a0ecc.jpeg",{"id":294,"title_md5":295,"publish_date":296,"author_md5":271,"is_original":4,"collection":5,"summary_md5":297,"cover_url":298,"cover_url_1_1":299},433,"b7284af2c7824b9e0866ceed0094b187","2023-07-16","a3d7417bb9de0ade6b15493600246eb1","article_res/cover/ca3482d96ae636eab811ceea3f688b18.jpeg","article_res/cover/5cc44dd7e13714ecd8e3df63140ee6b9.jpeg",{"id":301,"title_md5":302,"publish_date":303,"author_md5":304,"is_original":4,"collection":5,"summary_md5":305,"cover_url":306,"cover_url_1_1":307},606,"36cb8b728035142c0028927fce8778f7","2022-03-11","311a46cfdaa3afda544e9285644f70d7","a120438bc4f2dddfdc48b8a86a696e7d","article_res/cover/0c511c5b29ba194b2ac045116d982724.jpeg","article_res/cover/5ef367446a709f88314204585ffe6e40.jpeg",{"id":309,"title_md5":310,"publish_date":311,"author_md5":312,"is_original":4,"collection":313,"summary_md5":314,"cover_url":315,"cover_url_1_1":316},516,"6bded04ff8a6df8916544270fe2be523","2022-06-11","7051dc52c184c205e39aa54b4664ae9b","#Philosophy","80d0fecbe0d3fcacf83c0c174f9df23d","article_res/cover/ad548e2f51cbb747d1a54fb5757f5971.jpeg","article_res/cover/883e6ae75ab5933479f0ba27447695b4.jpeg",{"related":318,"small":358},[319,326,334,342,350],{"id":320,"publish_date":321,"is_original":23,"collection":5,"cover_url":322,"cover_url_1_1":323,"title":324,"summary":325,"author":28},188,"2024-08-24","article_res/cover/95eeaf5ffe276036db29cb6efc32da3d.jpeg","article_res/cover/350a09325e5ebff73e4dd5cae10392ba.jpeg","Ideogram 2.0 AI Poster Production Tool","Introducing Ideogram 2.0 — our most advanced text-to-image model, now available to all users for free.",{"id":327,"publish_date":328,"is_original":23,"collection":329,"cover_url":330,"cover_url_1_1":331,"title":332,"summary":333,"author":28},206,"2024-08-02","#Neuroscience #Nature","article_res/cover/0ac6998a6dd9e1acef02bffd48730b76.jpeg","article_res/cover/75eb9c3da162a166c7429ba78d31e1d8.jpeg","【Nature article】Is language for communication or for thought?","The limits of my language mean the limits of my world.  \n- Ludwig Wittgenstein",{"id":335,"publish_date":336,"is_original":23,"collection":337,"cover_url":338,"cover_url_1_1":339,"title":340,"summary":341,"author":28},278,"2024-05-11","#AI in Science #Microsoft #AI Agent","article_res/cover/fdffcb60430f70acf4ce36f44394b4da.jpeg","article_res/cover/8706eb4365dcde4cc2fbdf0b2f7ab42c.jpeg","【Microsoft Paper】Agent AI, Holistic Intelligence, Large Foundation Model (LFM)","Furthermore, it is emerging as a promising pathway towards Holistic Intelligence (HI).",{"id":343,"publish_date":344,"is_original":23,"collection":345,"cover_url":346,"cover_url_1_1":347,"title":348,"summary":349,"author":28},70,"2025-01-15","#Google #LLM","article_res/cover/b014765b22653d87d4b09437b3578b8d.jpeg","article_res/cover/714f4bec5c754f37ed7679e409f188f9.jpeg","Google has released TITANS, the successor to the Transformer architecture","Titans: Learning to Memorize at Test Time",{"id":351,"publish_date":352,"is_original":23,"collection":353,"cover_url":354,"cover_url_1_1":355,"title":356,"summary":357,"author":28},34,"2025-02-19","#LLM #Grok3 #DeepSeek #ChatGPT #Think","article_res/cover/825ed24949e3d283bf6ddb0e224023bd.jpeg","article_res/cover/2a174a1c0753acd1cc25a4808c5e589c.jpeg","How Different Large Models Like DeepSeek R1/ChatGPT o3/Grok3 Think","LLM Think",[359,365,371],{"title":10,"list":360},[361,362,363,364],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":366},[367,368,369,370],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":372},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646407290]