[{"data":1,"prerenderedAt":205},["ShallowReactive",2],{"DlFXI4Eibt_Bn9lrEZz1TYbHCWFZj3IvqwHQSEW-Exc":3,"tej8rMYDFbqC-Xe-2YAHUx_7n28R8y1f9mmEeygGKRQ":194},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"hot":39,"new":78,"banner":118,"data":143,"cache":193},[8,9,10],"Agent","OpenAI","LLM",[12,14,17,20,23,25,27,30,33,36],{"title":8,"total":13},39,{"title":15,"total":16},"Google",44,{"title":18,"total":19},"Nvidia",13,{"title":21,"total":22},"Claude",11,{"title":9,"total":24},35,{"title":10,"total":26},85,{"title":28,"total":29},"DeepSeek",9,{"title":31,"total":32},"OCR",1,{"title":34,"total":35},"Chat",7,{"title":37,"total":38},"Generator",116,[40,48,55,64,71],{"id":41,"publish_date":42,"is_original":4,"collection":5,"cover_url":43,"cover_url_1_1":44,"title":45,"summary":46,"author":47},557,"2022-04-29","article_res/cover/7a9b1375ed9bb298154981bae42b794d.jpeg","article_res/cover/afa281dd52bc0454e6735daa8e6b0706.jpeg","Translation and summary of Messari Report [2.8 Kristin Smith, Blockchain Association and Katie Haun, a16z]","We need unity and speed right now.","Translation",{"id":49,"publish_date":50,"is_original":4,"collection":5,"cover_url":51,"cover_url_1_1":52,"title":53,"summary":54,"author":47},531,"2022-05-25","article_res/cover/e8362057f8fa189594c60afdfaaeb6e5.jpeg","article_res/cover/8ea08d0d6fa7eee6b57ed4ec61b61ad6.jpeg","Decentralized Society: Finding Web3’s Soul / Decentralized Society: Finding the Soul of Web3 -7","Decentralization through Pluralism When analyzing ecosystems, it's desirable to measure how decentralized it is.",{"id":56,"publish_date":57,"is_original":32,"collection":58,"cover_url":59,"cover_url_1_1":60,"title":61,"summary":62,"author":63},127,"2024-11-14","#Google #AI Game #World Model #AI Story","article_res/cover/0233a875b7ec2debf59779e311547569.jpeg","article_res/cover/6ffddb6ae4914b3c699493311aa9f198.jpeg","Google Launches \"Unbounded\": A Generative Infinite Character Life Simulation Game","Unbounded: A Generative Infinite Game of Character Life Simulation","Renee's Entrepreneurial Journey",{"id":13,"publish_date":65,"is_original":32,"collection":66,"cover_url":67,"cover_url_1_1":68,"title":69,"summary":70,"author":63},"2025-02-14","#Deep Dive into LLMs #Andrej Karpathy #LLM #Tool Use #Hallucination","article_res/cover/11e858ad6b74dfa80f923d549b62855c.jpeg","article_res/cover/615e1b320f1fc163edc1d2d154a6de33.jpeg","Andrej Karpathy's in-depth explanation of LLM (Part 4): Hallucinations","hallucinations, tool use, knowledge/working memory",{"id":72,"publish_date":73,"is_original":4,"collection":5,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":47},579,"2022-04-07","article_res/cover/39387376ba28447af1eb40576b9df215.jpeg","article_res/cover/02727ede8551ed49901d0abe6d6305b7.jpeg","Messari Report Translation and Summary 【1-7 Surviving the Winter】","I’d be more cautious here: 10 year and 10 hour thinking only.",[79,87,95,103,111],{"id":80,"publish_date":81,"is_original":32,"collection":82,"cover_url":83,"cover_url_1_1":84,"title":85,"summary":86,"author":63},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":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},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":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},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":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},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":112,"publish_date":105,"is_original":32,"collection":113,"cover_url":114,"cover_url_1_1":115,"title":116,"summary":117,"author":63},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",[119,127,135],{"id":120,"publish_date":121,"is_original":32,"collection":122,"cover_url":123,"cover_url_1_1":124,"title":125,"summary":126,"author":63},160,"2024-10-04","#Philosophy","article_res/cover/496990c49211e8b7f996b7d39c18168e.jpeg","article_res/cover/14dbaa1ade9cb4316d5829423a900362.jpeg","Time","The fungus of the morning does not know the waxing and waning of the moon, and the cicada does not know the seasons; this is a short life. To the south of the state of Chu there is a dark spirit which regards five hundred years as spring and five hundred years as autumn. In ancient times there was a great tree called the Ming which regarded eight thousand years as spring and eight thousand years as autumn; this is a long life.",{"id":128,"publish_date":129,"is_original":32,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":63},98,"2024-12-17","#AI Video Generator #Sora #Pika","article_res/cover/3b86e85d03fff4f356a3e4cf2bb329c9.jpeg","article_res/cover/5fa5c20ad0b40f8f544d257c0ef02938.jpeg","Pika 2.0 video generation officially released: effect comparison with Sora","今天，我们推出了Pika 2.0模型。卓越的文字对齐效果。惊人的视觉表现。还有✨场景成分✨",{"id":136,"publish_date":137,"is_original":32,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":63},71,"2025-01-14","#Nvidia #World Foundation Model #Cosmos #Physical AI #Embodied AI","article_res/cover/feddf8c832dfb45d28804291f6a42a9e.jpeg","article_res/cover/d6bc2f1186d96b78228c2283a17a3645.jpeg","NVIDIA's Cosmos World Model","Cosmos World Foundation Model Platform for Physical AI",[144,163,188],{"title":8,"items":145},[146,147,155],{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},{"id":148,"publish_date":149,"is_original":32,"collection":150,"cover_url":151,"cover_url_1_1":152,"title":153,"summary":154,"author":63},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":156,"publish_date":157,"is_original":32,"collection":158,"cover_url":159,"cover_url_1_1":160,"title":161,"summary":162,"author":63},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",{"title":9,"items":164},[165,172,180],{"id":166,"publish_date":157,"is_original":32,"collection":167,"cover_url":168,"cover_url_1_1":169,"title":170,"summary":171,"author":63},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":173,"publish_date":174,"is_original":4,"collection":175,"cover_url":176,"cover_url_1_1":177,"title":178,"summary":179,"author":63},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":181,"publish_date":182,"is_original":4,"collection":183,"cover_url":184,"cover_url_1_1":185,"title":186,"summary":187,"author":63},417,"2023-08-24","#OpenAI","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.",{"title":10,"items":189},[190,191,192],{"id":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},{"id":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},true,{"code":4,"msg":5,"data":195},{"id":196,"publish_date":197,"is_original":32,"collection":198,"articles_id":199,"cover_url":200,"cover_url_1_1":201,"title":202,"summary":203,"author":63,"content":204},29,"2025-02-23","#Andrej Karpathy #Deep Dive into LLMs #LLM #RL","8-WgeM74XCF5hIh4B3yP2w","article_res/cover/0c295de74a30f6b7d07882c38a8050e6.jpeg","article_res/cover/0ad1dec70c276078c78df2ccffcae574.jpeg","Reinforcement learning - Andrej Karpathy in-depth explanation of LLM (Part 8)","强化学习","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Csection>\u003Cstrong style='color: rgb(0, 0, 0);font-size: 20px;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-variant-ligatures: normal;white-space-collapse: collapse;;width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;'>\u003Cspan leaf=\"\">The training process of LLM\u003C/span>\u003C/strong>\u003C/section>\u003Col style='box-sizing: border-box;margin: 8px 0px;;list-style-type: decimal;padding: 0px 0px 0px 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;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;' class=\"list-paddingleft-1\">\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Pre-training stage\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Build a basic model and learn from a large amount of internet document content.\u003C/span>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Supervised fine-tuning stage\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Through real dialogue data, the model is fine-tuned so that it can serve as an assistant.\u003C/span>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Reinforcement learning stage\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Optimize the model's decision-making and problem-solving abilities through practice and feedback.\u003C/span>\u003C/section>\u003C/li>\u003C/ol>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">After supervised fine-tuning, the model is already capable of handling some basic dialogue tasks, but we want it to become smarter and more adaptable. At this point,\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">reinforcement learning\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">(Reinforcement Learning, RL) becomes a critical training phase.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">Reinforcement learning differs from the previous two stages (pre-training and supervised fine-tuning), as it focuses on optimizing the model's decision-making ability through\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">practice and feedback\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">. Just like humans improving their skills through constant practice,\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">reinforcement learning allows the model to continuously try, adjust, and optimize its strategies in specific tasks.\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">。\u003C/span>\u003C/p>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;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;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Analogy of reinforcement learning to school education\u003C/span>\u003C/strong>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">To help understand the concept of reinforcement learning, we can compare it with\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">school education\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">:\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010232\" data-ratio=\"0.7527777777777778\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" src=\"https://res.cooltool.vip/article_res/assets/17423770171660.5586534490031039.png\">\u003C/section>\u003Cul style='box-sizing: border-box;margin: 8px 0px;;list-style-type: disc;padding: 0px 0px 0px 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;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;' class=\"list-paddingleft-1\">\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">First stage (pre-training)\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">is like students\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">gaining background knowledge by reading textbooks\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">. In this stage, the model learns knowledge from a large amount of text.\u003C/span>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Second stage (supervised fine-tuning)\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">is similar to\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">experts demonstrating how to solve problems\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">, during which the model learns how to handle dialogue tasks by\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">imitating experts\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">.\u003C/span>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;;\">\u003Csection style=\"box-sizing: border-box;;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Third stage (reinforcement learning)\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">is then the\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">practice stage\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">, where the model optimizes its decision-making and problem-solving abilities by solving\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">real-world problems\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">and receiving feedback,\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">continuously refining its own decisions and problem-solving capabilities\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">。\u003C/span>\u003C/section>\u003C/li>\u003C/ul>\u003Csection style=\"box-sizing:border-box;cursor:pointer;margin-top:5px;margin-bottom:5px;color:rgb(1, 1, 1);font-size:16px;line-height:1.8em;letter-spacing:0em;text-align:left;font-weight:normal;\">\u003Cspan style='color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;display: inline !important;float: none;'>\u003Cspan leaf=\"\">The process of training large language models is, in essence, very similar to training a child. The only difference lies in the fact that children learn chapters from books and undergo various training exercises, while we train AI in stages, designing training steps according to each stage's requirements.\u003C/span>\u003C/span>\u003C/section>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;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;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">The process of reinforcement learning: practice and feedback\u003C/span>\u003C/strong>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;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 reinforcement learning phase, the model no longer relies on solutions provided by experts, but instead\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">interacts with the environment and tries different strategies\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">, adjusting its behavior based on the feedback received. The goal of reinforcement learning is for the model to learn, through\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">exploration and practice\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">, how to make optimal decisions in specific tasks.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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 stage can be compared to\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">doing exercises\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Students face a series of practice questions,\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">attempting to solve them independently\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">, then adjusting their methods based on the results. This aligns with the\u003C/span>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">core idea of reinforcement learning\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: achieving optimal performance through repeated trials and continuous optimization.\u003C/span>\u003C/p>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;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;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Example\u003C/span>\u003C/strong>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">Suppose Emily bought three apples and two oranges, with each orange costing $2, and the total cost of all fruits being $13. What is the price of each apple? We can solve this problem using several different methods, and all methods will ultimately lead to the same answer — $3.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010233\" data-ratio=\"0.5138888888888888\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" src=\"https://res.cooltool.vip/article_res/assets/17423770173230.21662505890362427.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">Human data labelers are unclear about which type of dialogue should be added to the training set. Some dialogues may use system equations, others might express in plain English, or directly skip steps and provide answers.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">If GPT (e.g., ChatGPT) were to answer this question, it might choose to establish a variable system and then solve the problem.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.8608445297504799\" data-s=\"300,640\" data-type=\"png\" data-w=\"1042\" type=\"block\" data-imgfileid=\"100010234\" src=\"https://res.cooltool.vip/article_res/assets/17423770170620.5891665455504123.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">For us humans, certain tasks are easy, but for LLMs, they might be extremely difficult. The cognitive methods of humans and LLMs differ. Some token sequences may seem very simple to us, but for LLMs, they represent a huge leap; conversely, LLMs might perform very simply on tasks that appear complex to us, resulting in wasted tokens. Therefore, if we only care about the final answer and not how it is presented to humans, we cannot accurately determine how to annotate this example or what kind of solution to provide to the LLM.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">More importantly, the knowledge of LLMs differs from ours. LLMs actually possess extensive knowledge in mathematics, physics, chemistry, and other fields, surpassing our knowledge in some aspects. However, when we input information, we may inject knowledge that the model itself lacks, which could be a significant leap for the model and cause confusion. Thus, our cognition and the model's cognition differ, making it unclear how to annotate the most suitable solution for LLMs.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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 summary, we are not currently in an ideal state to create the most appropriate token sequence for LLMs. We hope to initialize the system through imitation, but the ultimate goal is for the LLM to discover the token sequence most suitable for itself. This requires reinforcement learning and trial-and-error processes, allowing the LLM to discover what kind of token sequence can reliably produce the correct answer.\u003C/span>\u003C/p>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;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;;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">How reinforcement learning works\u003C/span>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">On Hugging Face's inference platform, give the model a simple task, and the model generates answers based on this task.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"0.3453703703703704\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" data-imgfileid=\"100010235\" src=\"https://res.cooltool.vip/article_res/assets/17423770171660.04603669952884992.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">By continuously trying and reviewing each result, we can evaluate the model's performance. Each generated answer follows a different path, some leading to correct results, while others may fail. Ultimately, we aim to encourage solutions that produce correct answers and optimize the model's generation strategy through trial and error.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">Assume we have a prompt, and we attempt multiple answers in parallel. Some answers may be correct, marked in green, while others may fail, marked in red.\u003C/span>\u003C/p>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"1.0012091898428053\" data-s=\"300,640\" data-type=\"png\" data-w=\"827\" type=\"block\" data-imgfileid=\"100010236\" src=\"https://res.cooltool.vip/article_res/assets/17423770180070.11604292959229334.png\">\u003C/section>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">Suppose we generate 15 answers, with only 4 yielding the correct result. Now, our goal is to encourage the model to generate more solutions similar to the green ones. This means that for the red answers, the model went wrong at certain points, so these token sequences are not effective paths; the token sequences of the green answers produced the correct results, so we hope to use this type of answer more frequently in future prompts.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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=\"\">These answers are not designed by human labelers but are generated by the model in actual operations. The model discovers which token sequences yield correct answers through continuous attempts. It’s akin to a student reviewing their past answers, analyzing which methods are effective and which are not, and learning how to better solve similar problems.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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 this case, we can select the best answer among them — for instance, the shortest one or the one with the best visual effect — as the \"best answer.\" Then we will train the model to prefer this answer path. After parameter updates, the model will be more inclined to adopt this method when facing similar situations in the future.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;;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 training process involves a large number of different types of prompts, covering math, physics, and various other problems, with tens of thousands of prompts and answers. Therefore, as training progresses, the model finds out which token sequences can effectively yield correct answers through continuous trial and error. This is the core process of reinforcement learning: constantly guessing, verifying, and guiding future reasoning through more successful answers.\u003C/span>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",1752585427380]