{"id":10007086,"date":"2023-01-17T08:20:17","date_gmt":"2023-01-17T00:20:17","guid":{"rendered":"https:\/\/ityet.com\/?p=10007086"},"modified":"2023-01-17T08:20:17","modified_gmt":"2023-01-17T00:20:17","slug":"chatgpt-instructgpt%e8%af%a6%e8%a7%a3","status":"publish","type":"post","link":"https:\/\/itmax.cn\/?p=10007086","title":{"rendered":"ChatGPT\/InstructGPT\u8be6\u89e3"},"content":{"rendered":"<p><strong>\u4f5c\u8005\uff1a\u4eac\u4e1c\u96f6\u552e \u5218\u5ca9<\/strong><\/p>\n<h2>\u524d\u8a00<\/h2>\n<p>GPT\u7cfb\u5217\u662fOpenAI\u7684\u4e00\u7cfb\u5217\u9884\u8bad\u7ec3\u6587\u7ae0\uff0cGPT\u7684\u5168\u79f0\u662fGenerative Pre-Trained Transformer\uff0c\u987e\u540d\u601d\u4e49\uff0cGPT\u7684\u76ee\u7684\u5c31\u662f\u901a\u8fc7Transformer\u4e3a\u57fa\u7840\u6a21\u578b\uff0c\u4f7f\u7528\u9884\u8bad\u7ec3\u6280\u672f\u5f97\u5230\u901a\u7528\u7684\u6587\u672c\u6a21\u578b\u3002\u76ee\u524d\u5df2\u7ecf\u516c\u5e03\u8bba\u6587\u7684\u6709\u6587\u672c\u9884\u8bad\u7ec3GPT-1\uff0cGPT-2\uff0cGPT-3\uff0c\u4ee5\u53ca\u56fe\u50cf\u9884\u8bad\u7ec3iGPT\u3002\u636e\u4f20\u8fd8\u672a\u53d1\u5e03\u7684GPT-4\u662f\u4e00\u4e2a\u591a\u6a21\u6001\u6a21\u578b\u3002\u6700\u8fd1\u975e\u5e38\u706b\u7684ChatGPT\u548c\u4eca\u5e74\u5e74\u521d\u516c\u5e03\u7684[[1]](<a href=\"https:\/\/link.segmentfault.com\/?enc=RYgkrsNw8E7hDizquf9SJA%3D%3D.%2FAlP237Si68Wi2tI6saVn5cFj7J01LFtd3gKvDeSkFwN1%2F280wo6%2BHpFkAk9%2FnWI\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\u662f\u4e00\u5bf9\u59d0\u59b9\u6a21\u578b\uff0c\u662f\u5728GPT-4\u4e4b\u524d\u53d1\u5e03\u7684\u9884\u70ed\u6a21\u578b\uff0c\u6709\u65f6\u5019\u4e5f\u88ab\u53eb\u505aGPT3.5\u3002ChatGPT\u548cInstructGPT\u5728\u6a21\u578b\u7ed3\u6784\uff0c\u8bad\u7ec3\u65b9\u5f0f\u4e0a\u90fd\u5b8c\u5168\u4e00\u81f4\uff0c\u5373\u90fd\u4f7f\u7528\u4e86\u6307\u793a\u5b66\u4e60\uff08Instruction Learning\uff09\u548c\u4eba\u5de5\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60\uff08Reinforcement Learning from Human Feedback\uff0cRLHF\uff09\u6765\u6307\u5bfc\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5b83\u4eec\u4e0d\u540c\u7684\u4ec5\u4ec5\u662f\u91c7\u96c6\u6570\u636e\u7684\u65b9\u5f0f\u4e0a\u6709\u6240\u5dee\u5f02\u3002\u6240\u4ee5\u8981\u641e\u61c2ChatGPT\uff0c\u6211\u4eec\u5fc5\u987b\u8981\u5148\u8bfb\u61c2InstructGPT\u3002<\/p>\n<h2>1. \u80cc\u666f\u77e5\u8bc6<\/h2>\n<p>\u5728\u4ecb\u7ecdChatGPT\/InstructGPT\u4e4b\u524d\uff0c\u6211\u4eec\u5148\u4ecb\u7ecd\u5b83\u4eec\u4f9d\u8d56\u7684\u57fa\u7840\u7b97\u6cd5\u3002<\/p>\n<h3>1.1 GPT\u7cfb\u5217<\/h3>\n<p>\u57fa\u4e8e\u6587\u672c\u9884\u8bad\u7ec3\u7684GPT-1[[2]](<a href=\"https:\/\/link.segmentfault.com\/?enc=alUAJyGqdrC9bt7qmVguBg%3D%3D.%2FEAVmYiCPcEXTPNTZFTm6i6gqdIGJmd9FLVeF1xK1%2FdMg0mZcH6TQb7pbsmaXznT\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\uff0cGPT-2[[3]](<a href=\"https:\/\/link.segmentfault.com\/?enc=1mFjkexHos97Rd3NOxJ8jg%3D%3D.j5u1bC70gf9gTf%2Bx7MzBh1sxCRkyCKvKfN%2FWfdXUPRemY6H8uGuLIPqNG1kjdQLD\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\uff0cGPT-3[[4]](<a href=\"https:\/\/link.segmentfault.com\/?enc=NXxwK4y4Y7CU6sqEPOYREA%3D%3D.mlNHtRSdSh6HylutxN9QTngaC0XmzCsDIakQcwJWNTf1G8KBaKi%2BWy3moCfIwDlD\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\u4e09\u4ee3\u6a21\u578b\u90fd\u662f\u91c7\u7528\u7684\u4ee5Transformer\u4e3a\u6838\u5fc3\u7ed3\u6784\u7684\u6a21\u578b\uff08\u56fe1\uff09\uff0c\u4e0d\u540c\u7684\u662f\u6a21\u578b\u7684\u5c42\u6570\u548c\u8bcd\u5411\u91cf\u957f\u5ea6\u7b49\u8d85\u53c2\uff0c\u5b83\u4eec\u5177\u4f53\u7684\u5185\u5bb9\u5982\u88681\u3002<\/p>\n<p><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000043339752\" alt=\"\" title=\"\" loading=\"lazy\"><\/p>\n<p>\u56fe1\uff1aGPT\u7cfb\u5217\u7684\u6a21\u578b\u7ed3\u6784\uff08\u5176\u4e2dTrm\u662f\u4e00\u4e2aTransformer\u7ed3\u6784\uff09<\/p>\n<p>\u88681\uff1a\u5386\u4ee3GPT\u7684\u53d1\u5e03\u65f6\u95f4\uff0c\u53c2\u6570\u91cf\u4ee5\u53ca\u8bad\u7ec3\u91cf<\/p>\n<table>\n<thead>\n<tr>\n<th>\u6a21\u578b<\/th>\n<th>\u53d1\u5e03\u65f6\u95f4<\/th>\n<th>\u5c42\u6570<\/th>\n<th>\u5934\u6570<\/th>\n<th>\u8bcd\u5411\u91cf\u957f\u5ea6<\/th>\n<th>\u53c2\u6570\u91cf<\/th>\n<th>\u9884\u8bad\u7ec3\u6570\u636e\u91cf<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>GPT-1<\/td>\n<td>2018 \u5e74 6 \u6708<\/td>\n<td>12<\/td>\n<td>12<\/td>\n<td>768<\/td>\n<td>1.17 \u4ebf<\/td>\n<td>\u7ea6 5GB<\/td>\n<\/tr>\n<tr>\n<td>GPT-2<\/td>\n<td>2019 \u5e74 2 \u6708<\/td>\n<td>48<\/td>\n<td>&#8211;<\/td>\n<td>1600<\/td>\n<td>15 \u4ebf<\/td>\n<td>40GB<\/td>\n<\/tr>\n<tr>\n<td>GPT-3<\/td>\n<td>2020 \u5e74 5 \u6708<\/td>\n<td>96<\/td>\n<td>96<\/td>\n<td>12888<\/td>\n<td>1,750 \u4ebf<\/td>\n<td>45TB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>GPT-1\u6bd4BERT\u8bde\u751f\u7565\u65e9\u51e0\u4e2a\u6708\u3002\u5b83\u4eec\u90fd\u662f\u91c7\u7528\u4e86Transformer\u4e3a\u6838\u5fc3\u7ed3\u6784\uff0c\u4e0d\u540c\u7684\u662fGPT-1\u901a\u8fc7<strong>\u81ea\u5de6\u5411\u53f3\u751f\u6210\u5f0f<\/strong>\u7684\u6784\u5efa\u9884\u8bad\u7ec3\u4efb\u52a1\uff0c\u7136\u540e\u5f97\u5230\u4e00\u4e2a\u901a\u7528\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u8fd9\u4e2a\u6a21\u578b\u548cBERT\u4e00\u6837\u90fd\u53ef\u7528\u6765\u505a\u4e0b\u6e38\u4efb\u52a1\u7684\u5fae\u8c03\u3002GPT-1\u5f53\u65f6\u57289\u4e2aNLP\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86SOTA\u7684\u6548\u679c\uff0c\u4f46GPT-1\u4f7f\u7528\u7684\u6a21\u578b\u89c4\u6a21\u548c\u6570\u636e\u91cf\u90fd\u6bd4\u8f83\u5c0f\uff0c\u8fd9\u4e5f\u5c31\u4fc3\u4f7f\u4e86GPT-2\u7684\u8bde\u751f\u3002<\/p>\n<p>\u5bf9\u6bd4GPT-1\uff0cGPT-2\u5e76\u672a\u5728\u6a21\u578b\u7ed3\u6784\u4e0a\u5927\u4f5c\u6587\u7ae0\uff0c\u53ea\u662f\u4f7f\u7528\u4e86\u66f4\u591a\u53c2\u6570\u7684\u6a21\u578b\u548c\u66f4\u591a\u7684\u8bad\u7ec3\u6570\u636e\uff08\u88681\uff09\u3002GPT-2\u6700\u91cd\u8981\u7684\u601d\u60f3\u662f\u63d0\u51fa\u4e86\u201c<strong>\u6240\u6709\u7684\u6709\u76d1\u7763\u5b66\u4e60\u90fd\u662f\u65e0\u76d1\u7763\u8bed\u8a00\u6a21\u578b\u7684\u4e00\u4e2a\u5b50\u96c6<\/strong>\u201d\u7684\u601d\u60f3\uff0c\u8fd9\u4e2a\u601d\u60f3\u4e5f\u662f\u63d0\u793a\u5b66\u4e60\uff08Prompt Learning\uff09\u7684\u524d\u8eab\u3002GPT-2\u5728\u8bde\u751f\u4e4b\u521d\u4e5f\u5f15\u53d1\u4e86\u4e0d\u5c11\u7684\u8f70\u52a8\uff0c\u5b83\u751f\u6210\u7684\u65b0\u95fb\u8db3\u4ee5\u6b3a\u9a97\u5927\u591a\u6570\u4eba\u7c7b\uff0c\u8fbe\u5230\u4ee5\u5047\u4e71\u771f\u7684\u6548\u679c\u3002\u751a\u81f3\u5f53\u65f6\u88ab\u79f0\u4e3a\u201cAI\u754c\u6700\u5371\u9669\u7684\u6b66\u5668\u201d\uff0c\u5f88\u591a\u95e8\u6237\u7f51\u7ad9\u4e5f\u547d\u4ee4\u7981\u6b62\u4f7f\u7528GPT-2\u751f\u6210\u7684\u65b0\u95fb\u3002<\/p>\n<p>GPT-3\u88ab\u63d0\u51fa\u65f6\uff0c\u9664\u4e86\u5b83\u8fdc\u8d85GPT-2\u7684\u6548\u679c\u5916\uff0c\u5f15\u8d77\u66f4\u591a\u8ba8\u8bba\u7684\u662f\u5b831750\u4ebf\u7684\u53c2\u6570\u91cf\u3002GPT-3\u9664\u4e86\u80fd\u5b8c\u6210\u5e38\u89c1\u7684NLP\u4efb\u52a1\u5916\uff0c\u7814\u7a76\u8005\u610f\u5916\u7684\u53d1\u73b0GPT-3\u5728\u5199SQL\uff0cJavaScript\u7b49\u8bed\u8a00\u7684\u4ee3\u7801\uff0c\u8fdb\u884c\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\u4e0a\u4e5f\u6709\u4e0d\u9519\u7684\u8868\u73b0\u6548\u679c\u3002GPT-3\u7684\u8bad\u7ec3\u4f7f\u7528\u4e86\u60c5\u5883\u5b66\u4e60\uff08In-context Learning\uff09\uff0c\u5b83\u662f\u5143\u5b66\u4e60\uff08Meta-learning\uff09\u7684\u4e00\u79cd\uff0c\u5143\u5b66\u4e60\u7684\u6838\u5fc3\u601d\u60f3\u5728\u4e8e\u901a\u8fc7\u5c11\u91cf\u7684\u6570\u636e\u5bfb\u627e\u4e00\u4e2a\u5408\u9002\u7684\u521d\u59cb\u5316\u8303\u56f4\uff0c\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5728\u6709\u9650\u7684\u6570\u636e\u96c6\u4e0a\u5feb\u901f\u62df\u5408\uff0c\u5e76\u83b7\u5f97\u4e0d\u9519\u7684\u6548\u679c\u3002<\/p>\n<p>\u901a\u8fc7\u4e0a\u9762\u7684\u5206\u6790\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\u4ece\u6027\u80fd\u89d2\u5ea6\u4e0a\u8bb2\uff0cGPT\u6709\u4e24\u4e2a\u76ee\u6807\uff1a<\/p>\n<ol>\n<li>\u63d0\u5347\u6a21\u578b\u5728\u5e38\u89c1NLP\u4efb\u52a1\u4e0a\u7684\u8868\u73b0\u6548\u679c\uff1b<\/li>\n<li>\u63d0\u5347\u6a21\u578b\u5728\u5176\u4ed6\u975e\u5178\u578bNLP\u4efb\u52a1\uff08\u4f8b\u5982\u4ee3\u7801\u7f16\u5199\uff0c\u6570\u5b66\u8fd0\u7b97\uff09\u4e0a\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/li>\n<\/ol>\n<p>\u53e6\u5916\uff0c\u9884\u8bad\u7ec3\u6a21\u578b\u81ea\u8bde\u751f\u4e4b\u59cb\uff0c\u4e00\u4e2a\u5907\u53d7\u8bdf\u75c5\u7684\u95ee\u9898\u5c31\u662f\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u504f\u89c1\u6027\u3002\u56e0\u4e3a\u9884\u8bad\u7ec3\u6a21\u578b\u90fd\u662f\u901a\u8fc7\u6d77\u91cf\u6570\u636e\u5728\u8d85\u5927\u53c2\u6570\u91cf\u7ea7\u7684\u6a21\u578b\u4e0a\u8bad\u7ec3\u51fa\u6765\u7684\uff0c\u5bf9\u6bd4\u5b8c\u5168\u7531\u4eba\u5de5\u89c4\u5219\u63a7\u5236\u7684\u4e13\u5bb6\u7cfb\u7edf\u6765\u8bf4\uff0c\u9884\u8bad\u7ec3\u6a21\u578b\u5c31\u50cf\u4e00\u4e2a\u9ed1\u76d2\u5b50\u3002\u6ca1\u6709\u4eba\u80fd\u591f\u4fdd\u8bc1\u9884\u8bad\u7ec3\u6a21\u578b\u4e0d\u4f1a\u751f\u6210\u4e00\u4e9b\u5305\u542b\u79cd\u65cf\u6b67\u89c6\uff0c\u6027\u522b\u6b67\u89c6\u7b49\u5371\u9669\u5185\u5bb9\uff0c\u56e0\u4e3a\u5b83\u7684\u51e0\u5341GB\u751a\u81f3\u51e0\u5341TB\u7684\u8bad\u7ec3\u6570\u636e\u91cc\u51e0\u4e4e\u80af\u5b9a\u5305\u542b\u7c7b\u4f3c\u7684\u8bad\u7ec3\u6837\u672c\u3002\u8fd9\u4e5f\u5c31\u662fInstructGPT\u548cChatGPT\u7684\u63d0\u51fa\u52a8\u673a\uff0c\u8bba\u6587\u4e2d\u75283H\u6982\u62ec\u4e86\u5b83\u4eec\u7684\u4f18\u5316\u76ee\u6807\uff1a<\/p>\n<ul>\n<li>\u6709\u7528\u7684\uff08Helpful\uff09;<\/li>\n<li>\u53ef\u4fe1\u7684\uff08Honest\uff09;<\/li>\n<li>\u65e0\u5bb3\u7684\uff08Harmless\uff09\u3002<\/li>\n<\/ul>\n<p>OpenAI\u7684GPT\u7cfb\u5217\u6a21\u578b\u5e76\u6ca1\u6709\u5f00\u6e90\uff0c\u4f46\u662f\u5b83\u4eec\u63d0\u4f9b\u4e86\u6a21\u578b\u7684\u8bd5\u7528<a href=\"https:\/\/link.segmentfault.com\/?enc=gYLiSrR8EtVAxJZmVnAe6Q%3D%3D.FB2Kc25wJXNZH12QFF4XGMoEJismx0juaYF3InUpbFvJNP0Vn1aJ%2BD8qJ5pEsR5ySHOhIKKgbiB4gAeIMipBr5atH6Kf%2F2dS1vpE9lpj8Wc%3D\" rel=\"nofollow\">\u7f51\u7ad9<\/a>\uff0c\u6709\u6761\u4ef6\u7684\u540c\u5b66\u53ef\u4ee5\u81ea\u884c\u8bd5\u7528\u3002<\/p>\n<h3>1.2 \u6307\u793a\u5b66\u4e60\uff08Instruct Learning\uff09\u548c\u63d0\u793a\uff08Prompt Learning\uff09\u5b66\u4e60<\/h3>\n<p>\u6307\u793a\u5b66\u4e60\u662f\u8c37\u6b4cDeepmind\u7684Quoc V.Le\u56e2\u961f\u57282021\u5e74\u7684\u4e00\u7bc7\u540d\u4e3a\u300aFinetuned Language Models Are Zero-Shot Learners\u300b[[5]](<a href=\"https:\/\/link.segmentfault.com\/?enc=3ytjctXo397hIVdJyPip%2BA%3D%3D.sP%2FEVaAUtcdrVUc%2BYj2ZIhTmXI20uSDxhaKd1hjs7uQhZtwpjzyg4oO9AobbMxW%2F\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\u6587\u7ae0\u4e2d\u63d0\u51fa\u7684\u601d\u60f3\u3002\u6307\u793a\u5b66\u4e60\u548c\u63d0\u793a\u5b66\u4e60\u7684\u76ee\u7684\u90fd\u662f\u53bb\u6316\u6398\u8bed\u8a00\u6a21\u578b\u672c\u8eab\u5177\u5907\u7684\u77e5\u8bc6\u3002\u4e0d\u540c\u7684\u662fPrompt\u662f\u6fc0\u53d1\u8bed\u8a00\u6a21\u578b\u7684<strong>\u8865\u5168\u80fd\u529b<\/strong>\uff0c\u4f8b\u5982\u6839\u636e\u4e0a\u534a\u53e5\u751f\u6210\u4e0b\u534a\u53e5\uff0c\u6216\u662f\u5b8c\u5f62\u586b\u7a7a\u7b49\u3002Instruct\u662f\u6fc0\u53d1\u8bed\u8a00\u6a21\u578b\u7684\u7406\u89e3\u80fd\u529b\uff0c\u5b83\u901a\u8fc7\u7ed9\u51fa\u66f4\u660e\u663e\u7684\u6307\u4ee4\uff0c\u8ba9\u6a21\u578b\u53bb\u505a\u51fa\u6b63\u786e\u7684\u884c\u52a8\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e0b\u9762\u7684\u4f8b\u5b50\u6765\u7406\u89e3\u8fd9\u4e24\u4e2a\u4e0d\u540c\u7684\u5b66\u4e60\u65b9\u5f0f\uff1a<\/p>\n<ol>\n<li>\u63d0\u793a\u5b66\u4e60\uff1a\u7ed9\u5973\u670b\u53cb\u4e70\u4e86\u8fd9\u4e2a\u9879\u94fe\uff0c\u5979\u5f88\u559c\u6b22\uff0c\u8fd9\u4e2a\u9879\u94fe\u592a____\u4e86\u3002<\/li>\n<li>\u6307\u793a\u5b66\u4e60\uff1a\u5224\u65ad\u8fd9\u53e5\u8bdd\u7684\u60c5\u611f\uff1a\u7ed9\u5973\u670b\u53cb\u4e70\u4e86\u8fd9\u4e2a\u9879\u94fe\uff0c\u5979\u5f88\u559c\u6b22\u3002\u9009\u9879\uff1aA=\u597d\uff1bB=\u4e00\u822c\uff1bC=\u5dee\u3002<\/li>\n<\/ol>\n<p>\u6307\u793a\u5b66\u4e60\u7684\u4f18\u70b9\u662f\u5b83\u7ecf\u8fc7\u591a\u4efb\u52a1\u7684\u5fae\u8c03\u540e\uff0c\u4e5f\u80fd\u591f\u5728\u5176\u4ed6\u4efb\u52a1\u4e0a\u505azero-shot\uff0c\u800c\u63d0\u793a\u5b66\u4e60\u90fd\u662f\u9488\u5bf9\u4e00\u4e2a\u4efb\u52a1\u7684\u3002\u6cdb\u5316\u80fd\u529b\u4e0d\u5982\u6307\u793a\u5b66\u4e60\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u56fe2\u6765\u7406\u89e3\u5fae\u8c03\uff0c\u63d0\u793a\u5b66\u4e60\u548c\u6307\u793a\u5b66\u4e60\u3002<\/p>\n<p><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000043339753\" alt=\"\" title=\"\" loading=\"lazy\"><\/p>\n<p>\u56fe2\uff1a\u6a21\u578b\u5fae\u8c03\uff0c\u63d0\u793a\u5b66\u4e60\uff0c\u6307\u793a\u5b66\u4e60\u4e09\u8005\u7684\u5f02\u540c<\/p>\n<h3>1.3 \u4eba\u5de5\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60<\/h3>\n<p>\u56e0\u4e3a\u8bad\u7ec3\u5f97\u5230\u7684\u6a21\u578b\u5e76\u4e0d\u662f\u975e\u5e38\u53ef\u63a7\u7684\uff0c\u6a21\u578b\u53ef\u4ee5\u770b\u505a\u5bf9\u8bad\u7ec3\u96c6\u5206\u5e03\u7684\u4e00\u4e2a\u62df\u5408\u3002\u90a3\u4e48\u53cd\u9988\u5230\u751f\u6210\u6a21\u578b\u4e2d\uff0c\u8bad\u7ec3\u6570\u636e\u7684\u5206\u5e03\u4fbf\u662f\u5f71\u54cd\u751f\u6210\u5185\u5bb9\u7684\u8d28\u91cf\u6700\u91cd\u8981\u7684\u4e00\u4e2a\u56e0\u7d20\u3002\u6709\u65f6\u5019\u6211\u4eec\u5e0c\u671b\u6a21\u578b\u5e76\u4e0d\u4ec5\u4ec5\u53ea\u53d7\u8bad\u7ec3\u6570\u636e\u7684\u5f71\u54cd\uff0c\u800c\u662f\u4eba\u4e3a\u53ef\u63a7\u7684\uff0c\u4ece\u800c\u4fdd\u8bc1\u751f\u6210\u6570\u636e\u7684\u6709\u7528\u6027\uff0c\u771f\u5b9e\u6027\u548c\u65e0\u5bb3\u6027\u3002\u8bba\u6587\u4e2d\u591a\u6b21\u63d0\u5230\u4e86\u5bf9\u9f50\uff08Alignment\uff09\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u7406\u89e3\u4e3a\u6a21\u578b\u7684\u8f93\u51fa\u5185\u5bb9\u548c\u4eba\u7c7b\u559c\u6b22\u7684\u8f93\u51fa\u5185\u5bb9\u7684\u5bf9\u9f50\uff0c\u4eba\u7c7b\u559c\u6b22\u7684\u4e0d\u6b62\u5305\u62ec\u751f\u6210\u5185\u5bb9\u7684\u6d41\u7545\u6027\u548c\u8bed\u6cd5\u7684\u6b63\u786e\u6027\uff0c\u8fd8\u5305\u62ec\u751f\u6210\u5185\u5bb9\u7684\u6709\u7528\u6027\u3001\u771f\u5b9e\u6027\u548c\u65e0\u5bb3\u6027\u3002<\/p>\n<p>\u6211\u4eec\u77e5\u9053\u5f3a\u5316\u5b66\u4e60\u901a\u8fc7\u5956\u52b1\uff08Reward\uff09\u673a\u5236\u6765\u6307\u5bfc\u6a21\u578b\u8bad\u7ec3\uff0c\u5956\u52b1\u673a\u5236\u53ef\u4ee5\u770b\u505a\u4f20\u7edf\u6a21\u8bad\u7ec3\u673a\u5236\u7684\u635f\u5931\u51fd\u6570\u3002\u5956\u52b1\u7684\u8ba1\u7b97\u8981\u6bd4\u635f\u5931\u51fd\u6570\u66f4\u7075\u6d3b\u548c\u591a\u6837\uff08AlphaGO\u7684\u5956\u52b1\u662f\u5bf9\u5c40\u7684\u80dc\u8d1f\uff09\uff0c\u8fd9\u5e26\u6765\u7684\u4ee3\u4ef7\u662f\u5956\u52b1\u7684\u8ba1\u7b97\u662f\u4e0d\u53ef\u5bfc\u7684\uff0c\u56e0\u6b64\u4e0d\u80fd\u76f4\u63a5\u62ff\u6765\u505a\u53cd\u5411\u4f20\u64ad\u3002\u5f3a\u5316\u5b66\u4e60\u7684\u601d\u8def\u662f\u901a\u8fc7\u5bf9\u5956\u52b1\u7684\u5927\u91cf\u91c7\u6837\u6765\u62df\u5408\u635f\u5931\u51fd\u6570\uff0c\u4ece\u800c\u5b9e\u73b0\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u540c\u6837\u4eba\u7c7b\u53cd\u9988\u4e5f\u662f\u4e0d\u53ef\u5bfc\u7684\uff0c\u90a3\u4e48\u6211\u4eec\u4e5f\u53ef\u4ee5\u5c06\u4eba\u5de5\u53cd\u9988\u4f5c\u4e3a\u5f3a\u5316\u5b66\u4e60\u7684\u5956\u52b1\uff0c\u57fa\u4e8e\u4eba\u5de5\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60\u4fbf\u5e94\u8fd0\u800c\u751f\u3002<\/p>\n<p>RLHF\u6700\u65e9\u53ef\u4ee5\u8ffd\u6eaf\u5230Google\u57282017\u5e74\u53d1\u8868\u7684\u300aDeep Reinforcement Learning from Human Preferences\u300b[[6]](<a href=\"https:\/\/link.segmentfault.com\/?enc=2ViyMKEKlR%2BBG56hlnnH2A%3D%3D.ntjYwcVSuSd7Id4tX3mILhhCuHVs%2BFSyLxUBQqc%2BU3r5h9IdqJDJdI5t8H5ReGhO\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\uff0c\u5b83\u901a\u8fc7\u4eba\u5de5\u6807\u6ce8\u4f5c\u4e3a\u53cd\u9988\uff0c\u63d0\u5347\u4e86\u5f3a\u5316\u5b66\u4e60\u5728\u6a21\u62df\u673a\u5668\u4eba\u4ee5\u53ca\u96c5\u8fbe\u5229\u6e38\u620f\u4e0a\u7684\u8868\u73b0\u6548\u679c\u3002<\/p>\n<p>![]()<\/p>\n<p>\u56fe3\uff1a\u4eba\u5de5\u53cd\u9988\u7684\u5f3a\u5316\u5b66\u4e60\u7684\u57fa\u672c\u539f\u7406<\/p>\n<p>InstructGPT\/ChatGPT\u4e2d\u8fd8\u7528\u5230\u4e86\u5f3a\u5316\u5b66\u4e60\u4e2d\u4e00\u4e2a\u7ecf\u5178\u7684\u7b97\u6cd5\uff1aOpenAI\u63d0\u51fa\u7684\u6700\u8fd1\u7b56\u7565\u4f18\u5316\uff08Proximal Policy Optimization\uff0cPPO\uff09[[7]](<a href=\"https:\/\/link.segmentfault.com\/?enc=wKr8bF0gxfUsk6jYdgVtTg%3D%3D.seTpzUPnN8OmbPsr31XVL2yISOK1%2F0XSoNu7AMDtIbnyLdSZ6Zca4yuQ5PnqHvJH\" rel=\"nofollow\">https:\/\/zhuanlan.zhihu.com\/p\/&#8230;<\/a>)\u3002PPO\u7b97\u6cd5\u662f\u4e00\u79cd\u65b0\u578b\u7684Policy Gradient\u7b97\u6cd5\uff0cPolicy Gradient\u7b97\u6cd5\u5bf9\u6b65\u957f\u5341\u5206\u654f\u611f\uff0c\u4f46\u662f\u53c8\u96be\u4ee5\u9009\u62e9\u5408\u9002\u7684\u6b65\u957f\uff0c\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u65b0\u65e7\u7b56\u7565\u7684\u7684\u53d8\u5316\u5dee\u5f02\u5982\u679c\u8fc7\u5927\u5219\u4e0d\u5229\u4e8e\u5b66\u4e60\u3002PPO\u63d0\u51fa\u4e86\u65b0\u7684\u76ee\u6807\u51fd\u6570\u53ef\u4ee5\u5728\u591a\u4e2a\u8bad\u7ec3\u6b65\u9aa4\u5b9e\u73b0\u5c0f\u6279\u91cf\u7684\u66f4\u65b0\uff0c\u89e3\u51b3\u4e86Policy Gradient\u7b97\u6cd5\u4e2d\u6b65\u957f\u96be\u4ee5\u786e\u5b9a\u7684\u95ee\u9898\u3002\u5176\u5b9eTRPO\u4e5f\u662f\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u601d\u60f3\u4f46\u662f\u76f8\u6bd4\u4e8eTRPO\u7b97\u6cd5PPO\u7b97\u6cd5\u66f4\u5bb9\u6613\u6c42\u89e3\u3002<\/p>\n<h2>2. InstructGPT\/ChatGPT\u539f\u7406\u89e3\u8bfb<\/h2>\n<p>\u6709\u4e86\u4e0a\u9762\u8fd9\u4e9b\u57fa\u7840\u77e5\u8bc6\uff0c\u6211\u4eec\u518d\u53bb\u4e86\u89e3InstructGPT\u548cChatGPT\u5c31\u4f1a\u7b80\u5355\u5f88\u591a\u3002\u7b80\u5355\u6765\u8bf4\uff0cInstructGPT\/ChatGPT\u90fd\u662f\u91c7\u7528\u4e86<strong>GPT-3<\/strong>\u7684\u7f51\u7edc\u7ed3\u6784\uff0c\u901a\u8fc7<strong>\u6307\u793a\u5b66\u4e60<\/strong>\u6784\u5efa\u8bad\u7ec3\u6837\u672c\u6765\u8bad\u7ec3\u4e00\u4e2a\u53cd\u5e94\u9884\u6d4b\u5185\u5bb9\u6548\u679c\u7684\u5956\u52b1\u6a21\u578b\uff08RM\uff09\uff0c\u6700\u540e\u901a\u8fc7\u8fd9\u4e2a\u5956\u52b1\u6a21\u578b\u7684\u6253\u5206\u6765\u6307\u5bfc\u5f3a\u5316\u5b66\u4e60\u6a21\u578b\u7684\u8bad\u7ec3\u3002InstructGPT\/ChatGPT\u7684\u8bad\u7ec3\u6d41\u7a0b\u5982\u56fe4\u6240\u793a\u3002<\/p>\n<p><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000043339754\" alt=\"\" title=\"\" loading=\"lazy\"><\/p>\n<p>\u56fe4\uff1aInstructGPT\u7684\u8ba1\u7b97\u6d41\u7a0b\uff1a\uff081\uff09\u6709\u76d1\u7763\u5fae\u8c03\uff08SFT\uff09\uff1b\uff082\uff09\u5956\u52b1\u6a21\u578b\uff08RM\uff09\u8bad\u7ec3\uff1b\uff083\uff09\u901a\u8fc7PPO\u6839\u636e\u5956\u52b1\u6a21\u578b\u8fdb\u884c\u5f3a\u5316\u5b66\u4e60\u3002<\/p>\n<p>\u4ece\u56fe4\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff0cInstructGPT\/ChatGPT\u7684\u8bad\u7ec3\u53ef\u4ee5\u5206\u62103\u6b65\uff0c\u5176\u4e2d\u7b2c2\u6b65\u548c\u7b2c3\u6b65\u662f\u7684\u5956\u52b1\u6a21\u578b\u548c\u5f3a\u5316\u5b66\u4e60\u7684SFT\u6a21\u578b\u53ef\u4ee5\u53cd\u590d\u8fed\u4ee3\u4f18\u5316\u3002<\/p>\n<ol>\n<li>\u6839\u636e\u91c7\u96c6\u7684SFT\u6570\u636e\u96c6\u5bf9GPT-3\u8fdb\u884c\u6709\u76d1\u7763\u7684\u5fae\u8c03\uff08Supervised FineTune\uff0cSFT\uff09\uff1b<\/li>\n<li>\u6536\u96c6\u4eba\u5de5\u6807\u6ce8\u7684\u5bf9\u6bd4\u6570\u636e\uff0c\u8bad\u7ec3\u5956\u52b1\u6a21\u578b\uff08Reword Model\uff0cRM\uff09\uff1b<\/li>\n<li>\u4f7f\u7528RM\u4f5c\u4e3a\u5f3a\u5316\u5b66\u4e60\u7684\u4f18\u5316\u76ee\u6807\uff0c\u5229\u7528PPO\u7b97\u6cd5\u5fae\u8c03SFT\u6a21\u578b\u3002<\/li>\n<\/ol>\n<p>\u6839\u636e\u56fe4\uff0c\u6211\u4eec\u5c06\u5206\u522b\u4ecb\u7ecdInstructGPT\/ChatGPT\u7684\u6570\u636e\u96c6\u91c7\u96c6\u548c\u6a21\u578b\u8bad\u7ec3\u4e24\u4e2a\u65b9\u9762\u7684\u5185\u5bb9\u3002<\/p>\n<h3>2.1 \u6570\u636e\u96c6\u91c7\u96c6<\/h3>\n<p>\u5982\u56fe4\u6240\u793a\uff0cInstructGPT\/ChatGPT\u7684\u8bad\u7ec3\u5206\u62103\u6b65\uff0c\u6bcf\u4e00\u6b65\u9700\u8981\u7684\u6570\u636e\u4e5f\u6709\u4e9b\u8bb8\u5dee\u5f02\uff0c\u4e0b\u9762\u6211\u4eec\u5206\u522b\u4ecb\u7ecd\u5b83\u4eec\u3002<\/p>\n<h4>2.1.1 SFT\u6570\u636e\u96c6<\/h4>\n<p>SFT\u6570\u636e\u96c6\u662f\u7528\u6765\u8bad\u7ec3\u7b2c1\u6b65\u6709\u76d1\u7763\u7684\u6a21\u578b\uff0c\u5373\u4f7f\u7528\u91c7\u96c6\u7684\u65b0\u6570\u636e\uff0c\u6309\u7167GPT-3\u7684\u8bad\u7ec3\u65b9\u5f0f\u5bf9GPT-3\u8fdb\u884c\u5fae\u8c03\u3002\u56e0\u4e3aGPT-3\u662f\u4e00\u4e2a\u57fa\u4e8e\u63d0\u793a\u5b66\u4e60\u7684\u751f\u6210\u6a21\u578b\uff0c\u56e0\u6b64SFT\u6570\u636e\u96c6\u4e5f\u662f\u7531\u63d0\u793a-\u7b54\u590d\u5bf9\u7ec4\u6210\u7684\u6837\u672c\u3002SFT\u6570\u636e\u4e00\u90e8\u5206\u6765\u81ea\u4f7f\u7528OpenAI\u7684PlayGround\u7684\u7528\u6237\uff0c\u53e6\u4e00\u90e8\u5206\u6765\u81eaOpenAI\u96c7\u4f63\u768440\u540d\u6807\u6ce8\u5de5\uff08labeler\uff09\u3002\u5e76\u4e14\u4ed6\u4eec\u5bf9labeler\u8fdb\u884c\u4e86\u57f9\u8bad\u3002\u5728\u8fd9\u4e2a\u6570\u636e\u96c6\u4e2d\uff0c\u6807\u6ce8\u5de5\u7684\u5de5\u4f5c\u662f\u6839\u636e\u5185\u5bb9\u81ea\u5df1\u7f16\u5199\u6307\u793a\uff0c\u5e76\u4e14\u8981\u6c42\u7f16\u5199\u7684\u6307\u793a\u6ee1\u8db3\u4e0b\u9762\u4e09\u70b9\uff1a<\/p>\n<ul>\n<li><strong>\u7b80\u5355\u4efb\u52a1<\/strong>\uff1alabeler\u7ed9\u51fa\u4efb\u610f\u4e00\u4e2a\u7b80\u5355\u7684\u4efb\u52a1\uff0c\u540c\u65f6\u8981\u786e\u4fdd\u4efb\u52a1\u7684\u591a\u6837\u6027\uff1b<\/li>\n<li><strong>Few-shot\u4efb\u52a1<\/strong>\uff1alabeler\u7ed9\u51fa\u4e00\u4e2a\u6307\u793a\uff0c\u4ee5\u53ca\u8be5\u6307\u793a\u7684\u591a\u4e2a\u67e5\u8be2-\u76f8\u5e94\u5bf9\uff1b<\/li>\n<li><strong>\u7528\u6237\u76f8\u5173\u7684<\/strong>\uff1a\u4ece\u63a5\u53e3\u4e2d\u83b7\u53d6\u7528\u4f8b\uff0c\u7136\u540e\u8ba9labeler\u6839\u636e\u8fd9\u4e9b\u7528\u4f8b\u7f16\u5199\u6307\u793a\u3002<\/li>\n<\/ul>\n<h4>2.1.2 RM\u6570\u636e\u96c6<\/h4>\n<p>RM\u6570\u636e\u96c6\u7528\u6765\u8bad\u7ec3\u7b2c2\u6b65\u7684\u5956\u52b1\u6a21\u578b\uff0c\u6211\u4eec\u4e5f\u9700\u8981\u4e3aInstructGPT\/ChatGPT\u7684\u8bad\u7ec3\u8bbe\u7f6e\u4e00\u4e2a\u5956\u52b1\u76ee\u6807\u3002\u8fd9\u4e2a\u5956\u52b1\u76ee\u6807\u4e0d\u5fc5\u53ef\u5bfc\uff0c\u4f46\u662f\u4e00\u5b9a\u8981\u5c3d\u53ef\u80fd\u5168\u9762\u4e14\u771f\u5b9e\u7684\u5bf9\u9f50\u6211\u4eec\u9700\u8981\u6a21\u578b\u751f\u6210\u7684\u5185\u5bb9\u3002\u5f88\u81ea\u7136\u7684\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4eba\u5de5\u6807\u6ce8\u7684\u65b9\u5f0f\u6765\u63d0\u4f9b\u8fd9\u4e2a\u5956\u52b1\uff0c\u901a\u8fc7\u4eba\u5de5\u5bf9\u53ef\u4ee5\u7ed9\u90a3\u4e9b\u6d89\u53ca\u504f\u89c1\u7684\u751f\u6210\u5185\u5bb9\u66f4\u4f4e\u7684\u5206\u4ece\u800c\u9f13\u52b1\u6a21\u578b\u4e0d\u53bb\u751f\u6210\u8fd9\u4e9b\u4eba\u7c7b\u4e0d\u559c\u6b22\u7684\u5185\u5bb9\u3002InstructGPT\/ChatGPT\u7684\u505a\u6cd5\u662f\u5148\u8ba9\u6a21\u578b\u751f\u6210\u4e00\u6279\u5019\u9009\u6587\u672c\uff0c\u8ba9\u540e\u901a\u8fc7labeler\u6839\u636e\u751f\u6210\u6570\u636e\u7684\u8d28\u91cf\u5bf9\u8fd9\u4e9b\u751f\u6210\u5185\u5bb9\u8fdb\u884c\u6392\u5e8f\u3002<\/p>\n<h4>2.1.3 PPO\u6570\u636e\u96c6<\/h4>\n<p>InstructGPT\u7684PPO\u6570\u636e\u6ca1\u6709\u8fdb\u884c\u6807\u6ce8\uff0c\u5b83\u5747\u6765\u81eaGPT-3\u7684API\u7684\u7528\u6237\u3002\u65e2\u53c8\u4e0d\u540c\u7528\u6237\u63d0\u4f9b\u7684\u4e0d\u540c\u79cd\u7c7b\u7684\u751f\u6210\u4efb\u52a1\uff0c\u5176\u4e2d\u5360\u6bd4\u6700\u9ad8\u7684\u5305\u62ec\u751f\u6210\u4efb\u52a1\uff0845.6%\uff09\uff0cQA\uff0812.4%\uff09\uff0c\u5934\u8111\u98ce\u66b4\uff0811.2%\uff09\uff0c\u5bf9\u8bdd\uff088.4%\uff09\u7b49\u3002<\/p>\n<h4>2.1.4 \u6570\u636e\u5206\u6790<\/h4>\n<p>\u56e0\u4e3aInstructGPT\/ChatGPT\u662f\u5728GPT-3\u57fa\u7840\u4e0a\u505a\u7684\u5fae\u8c03\uff0c\u800c\u4e14\u56e0\u4e3a\u6d89\u53ca\u4e86\u4eba\u5de5\u6807\u6ce8\uff0c\u5b83\u4eec\u6570\u636e\u603b\u91cf\u5e76\u4e0d\u5927\uff0c\u88682\u5c55\u793a\u4e86\u4e09\u4efd\u6570\u636e\u7684\u6765\u6e90\u53ca\u5176\u6570\u636e\u91cf\u3002<\/p>\n<p><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000043339755\" alt=\"\" title=\"\" loading=\"lazy\"><\/p>\n<p>\u88682\uff1aInstructGPT\u7684\u6570\u636e\u5206\u5e03<\/p>\n<p>\u8bba\u6587\u7684\u9644\u5f55A\u5bf9\u6570\u636e\u7684\u5206\u5e03\u8fdb\u884c\u4e86\u66f4\u8be6\u7ec6\u7684\u8ba8\u8bba\uff0c\u8fd9\u91cc\u6211\u5217\u51fa\u51e0\u4e2a\u53ef\u80fd\u5f71\u54cd\u6a21\u578b\u6548\u679c\u7684\u51e0\u9879\uff1a<\/p>\n<ul>\n<li>\u6570\u636e\u4e2d96%\u4ee5\u4e0a\u662f\u82f1\u6587\uff0c\u5176\u5b8320\u4e2a\u8bed\u79cd\u4f8b\u5982\u4e2d\u6587\uff0c\u6cd5\u8bed\uff0c\u897f\u73ed\u7259\u8bed\u7b49\u52a0\u8d77\u6765\u4e0d\u52304%\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4InstructGPT\/ChatGPT\u80fd\u8fdb\u884c\u5176\u5b83\u8bed\u79cd\u7684\u751f\u6210\uff0c\u4f46\u6548\u679c\u5e94\u8be5\u8fdc\u4e0d\u5982\u82f1\u6587\uff1b<\/li>\n<li>\u63d0\u793a\u79cd\u7c7b\u5171\u67099\u79cd\uff0c\u800c\u4e14\u7edd\u5927\u591a\u6570\u662f\u751f\u6210\u7c7b\u4efb\u52a1\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6a21\u578b\u6709\u8986\u76d6\u4e0d\u5230\u7684\u4efb\u52a1\u7c7b\u578b\uff1b<\/li>\n<li>40\u540d\u5916\u5305\u5458\u5de5\u6765\u81ea\u7f8e\u56fd\u548c\u4e1c\u5357\u4e9a\uff0c\u5206\u5e03\u6bd4\u8f83\u96c6\u4e2d\u4e14\u4eba\u6570\u8f83\u5c11\uff0c InstructGPT\/ChatGPT\u7684\u76ee\u6807\u662f\u8bad\u7ec3\u4e00\u4e2a\u4ef7\u503c\u89c2\u6b63\u786e\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u5b83\u7684\u4ef7\u503c\u89c2\u662f\u7531\u8fd940\u4e2a\u5916\u5305\u5458\u5de5\u7684\u4ef7\u503c\u89c2\u7ec4\u5408\u800c\u6210\u3002\u800c\u8fd9\u4e2a\u6bd4\u8f83\u7a84\u7684\u5206\u5e03\u53ef\u80fd\u4f1a\u751f\u6210\u4e00\u4e9b\u5176\u4ed6\u5730\u533a\u6bd4\u8f83\u5728\u610f\u7684\u6b67\u89c6\uff0c\u504f\u89c1\u95ee\u9898\u3002<\/li>\n<\/ul>\n<p>\u6b64\u5916\uff0cChatGPT\u7684\u535a\u5ba2\u4e2d\u8bb2\u5230ChatGPT\u548cInstructGPT\u7684\u8bad\u7ec3\u65b9\u5f0f\u76f8\u540c\uff0c\u4e0d\u540c\u70b9\u4ec5\u4ec5\u662f\u5b83\u4eec\u91c7\u96c6\u6570\u636e\u4e0a\u6709\u6240\u4e0d\u540c\uff0c\u4f46\u662f\u5e76\u6ca1\u6709\u66f4\u591a\u7684\u8d44\u6599\u6765\u8bb2\u6570\u636e\u91c7\u96c6\u4e0a\u6709\u54ea\u4e9b\u7ec6\u8282\u4e0a\u7684\u4e0d\u540c\u3002\u8003\u8651\u5230ChatGPT\u4ec5\u4ec5\u88ab\u7528\u5728\u5bf9\u8bdd\u9886\u57df\uff0c\u8fd9\u91cc\u6211\u731c\u6d4bChatGPT\u5728\u6570\u636e\u91c7\u96c6\u4e0a\u6709\u4e24\u4e2a\u4e0d\u540c\uff1a1. \u63d0\u9ad8\u4e86\u5bf9\u8bdd\u7c7b\u4efb\u52a1\u7684\u5360\u6bd4\uff1b2. \u5c06\u63d0\u793a\u7684\u65b9\u5f0f\u8f6c\u6362Q&#038;A\u7684\u65b9\u5f0f\u3002\u5f53\u7136\u8fd9\u91cc\u4e5f\u4ec5\u4ec5\u662f\u731c\u6d4b\uff0c\u66f4\u51c6\u786e\u7684\u63cf\u8ff0\u8981\u7b49\u5230ChatGPT\u7684\u8bba\u6587\u3001\u6e90\u7801\u7b49\u66f4\u8be6\u7ec6\u7684\u8d44\u6599\u516c\u5e03\u6211\u4eec\u624d\u80fd\u77e5\u9053\u3002<\/p>\n<h3>2.2 \u8bad\u7ec3\u4efb\u52a1<\/h3>\n<p>\u6211\u4eec\u521a\u4ecb\u7ecd\u5230InstructGPT\/ChatGPT\u6709\u4e09\u6b65\u8bad\u7ec3\u65b9\u5f0f\u3002\u8fd9\u4e09\u6b65\u8bad\u7ec3\u4f1a\u6d89\u53ca\u4e09\u4e2a\u6a21\u578b\uff1aSFT\uff0cRM\u4ee5\u53caPPO\uff0c\u4e0b\u9762\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u5b83\u4eec\u3002<\/p>\n<h4>2.2.1 \u6709\u76d1\u7763\u5fae\u8c03\uff08SFT\uff09<\/h4>\n<p>\u8fd9\u4e00\u6b65\u7684\u8bad\u7ec3\u548cGPT-3\u4e00\u81f4\uff0c\u800c\u4e14\u4f5c\u8005\u53d1\u73b0\u8ba9\u6a21\u578b\u9002\u5f53\u8fc7\u62df\u5408\u6709\u52a9\u4e8e\u540e\u9762\u4e24\u6b65\u7684\u8bad\u7ec3\u3002<\/p>\n<h4>2.2.2 \u5956\u52b1\u6a21\u578b\uff08RM\uff09<\/h4>\n<p>\u56e0\u4e3a\u8bad\u7ec3RM\u7684\u6570\u636e\u662f\u4e00\u4e2alabeler\u6839\u636e\u751f\u6210\u7ed3\u679c\u6392\u5e8f\u7684\u5f62\u5f0f\uff0c\u6240\u4ee5\u5b83\u53ef\u4ee5\u770b\u505a\u4e00\u4e2a\u56de\u5f52\u6a21\u578b\u3002RM\u7ed3\u6784\u662f\u5c06SFT\u8bad\u7ec3\u540e\u7684\u6a21\u578b\u7684\u6700\u540e\u7684\u5d4c\u5165\u5c42\u53bb\u6389\u540e\u7684\u6a21\u578b\u3002\u5b83\u7684\u8f93\u5165\u662fprompt\u548cReponse\uff0c\u8f93\u51fa\u662f\u5956\u52b1\u503c\u3002\u5177\u4f53\u7684\u8bb2\uff0c\u5bf9\u5f08\u6bcf\u4e2aprompt\uff0cInstructGPT\/ChatGPT\u4f1a\u968f\u673a\u751f\u6210\u00a0K\u00a0\u4e2a\u8f93\u51fa\uff08\u00a04\u2264K\u22649\u00a0\uff09\uff0c\u7136\u540e\u5b83\u4eec\u5411\u6bcf\u4e2alabeler\u6210\u5bf9\u7684\u5c55\u793a\u8f93\u51fa\u7ed3\u679c\uff0c\u4e5f\u5c31\u662f\u6bcf\u4e2aprompt\u5171\u5c55\u793a\u00a0CK2\u00a0\u4e2a\u7ed3\u679c\uff0c\u7136\u540e\u7528\u6237\u4ece\u4e2d\u9009\u62e9\u6548\u679c\u66f4\u597d\u7684\u8f93\u51fa\u3002\u5728\u8bad\u7ec3\u65f6\uff0cInstructGPT\/ChatGPT\u5c06\u6bcf\u4e2aprompt\u7684\u00a0CK2\u00a0\u4e2a\u54cd\u5e94\u5bf9\u4f5c\u4e3a\u4e00\u4e2abatch\uff0c\u8fd9\u79cd\u6309prompt\u4e3abatch\u7684\u8bad\u7ec3\u65b9\u5f0f\u8981\u6bd4\u4f20\u7edf\u7684\u6309\u6837\u672c\u4e3abatch\u7684\u65b9\u5f0f\u66f4\u4e0d\u5bb9\u6613\u8fc7\u62df\u5408\uff0c\u56e0\u4e3a\u8fd9\u79cd\u65b9\u5f0f\u6bcf\u4e2aprompt\u4f1a\u4e14\u4ec5\u4f1a\u8f93\u5165\u5230\u6a21\u578b\u4e2d\u4e00\u6b21\u3002<\/p>\n<p>\u5956\u52b1\u6a21\u578b\u7684\u635f\u5931\u51fd\u6570\u8868\u793a\u4e3a\u5f0f(1)\u3002\u8fd9\u4e2a\u635f\u5931\u51fd\u6570\u7684\u76ee\u6807\u662f\u6700\u5927\u5316labeler\u66f4\u559c\u6b22\u7684\u54cd\u5e94\u548c\u4e0d\u559c\u6b22\u7684\u54cd\u5e94\u4e4b\u95f4\u7684\u5dee\u503c\u3002<\/p>\n<p>(1)loss\u2061(\u03b8)=\u22121(K2)E(x,yw,yl)\u223cD[log\u2061(\u03c3(r\u03b8(x,yw)\u2212r\u03b8(x,yl)))]<\/p>\n<p>\u5176\u4e2d\u00a0r\u03b8(x,y)\u00a0\u662f\u63d0\u793a\u00a0x\u00a0\u548c\u54cd\u5e94\u00a0y\u00a0\u5728\u53c2\u6570\u4e3a\u00a0\u03b8\u00a0\u7684\u5956\u52b1\u6a21\u578b\u4e0b\u7684\u5956\u52b1\u503c\uff0c\u00a0yw\u00a0\u662flabeler\u66f4\u559c\u6b22\u7684\u54cd\u5e94\u7ed3\u679c\uff0c\u00a0yl\u00a0\u662flabeler\u4e0d\u559c\u6b22\u7684\u54cd\u5e94\u7ed3\u679c\u3002\u00a0D\u00a0\u662f\u6574\u4e2a\u8bad\u7ec3\u6570\u636e\u96c6\u3002<\/p>\n<h4>2.2.3 \u5f3a\u5316\u5b66\u4e60\u6a21\u578b\uff08PPO\uff09<\/h4>\n<p>\u5f3a\u5316\u5b66\u4e60\u548c\u9884\u8bad\u7ec3\u6a21\u578b\u662f\u6700\u8fd1\u4e24\u5e74\u6700\u4e3a\u706b\u70ed\u7684AI\u65b9\u5411\u4e4b\u4e8c\uff0c\u4e4b\u524d\u4e0d\u5c11\u79d1\u7814\u5de5\u4f5c\u8005\u8bf4\u5f3a\u5316\u5b66\u4e60\u5e76\u4e0d\u662f\u4e00\u4e2a\u975e\u5e38\u9002\u5408\u5e94\u7528\u5230\u9884\u8bad\u7ec3\u6a21\u578b\u4e2d\uff0c\u56e0\u4e3a\u5f88\u96be\u901a\u8fc7\u6a21\u578b\u7684\u8f93\u51fa\u5185\u5bb9\u5efa\u7acb\u5956\u52b1\u673a\u5236\u3002\u800cInstructGPT\/ChatGPT\u53cd\u76f4\u89c9\u7684\u505a\u5230\u4e86\u8fd9\u70b9\uff0c\u5b83\u901a\u8fc7\u7ed3\u5408\u4eba\u5de5\u6807\u6ce8\uff0c\u5c06\u5f3a\u5316\u5b66\u4e60\u5f15\u5165\u5230\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u662f\u8fd9\u4e2a\u7b97\u6cd5\u6700\u5927\u7684\u521b\u65b0\u70b9\u3002<\/p>\n<p>\u5982\u88682\u6240\u793a\uff0cPPO\u7684\u8bad\u7ec3\u96c6\u5b8c\u5168\u6765\u81eaAPI\u3002\u5b83\u901a\u8fc7\u7b2c2\u6b65\u5f97\u5230\u7684\u5956\u52b1\u6a21\u578b\u6765\u6307\u5bfcSFT\u6a21\u578b\u7684\u7ee7\u7eed\u8bad\u7ec3\u3002\u5f88\u591a\u65f6\u5019\u5f3a\u5316\u5b66\u4e60\u662f\u975e\u5e38\u96be\u8bad\u7ec3\u7684\uff0cInstructGPT\/ChatGPT\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5c31\u9047\u5230\u4e86\u4e24\u4e2a\u95ee\u9898\uff1a<\/p>\n<ol>\n<li>\u95ee\u98981\uff1a\u968f\u7740\u6a21\u578b\u7684\u66f4\u65b0\uff0c\u5f3a\u5316\u5b66\u4e60\u6a21\u578b\u4ea7\u751f\u7684\u6570\u636e\u548c\u8bad\u7ec3\u5956\u52b1\u6a21\u578b\u7684\u6570\u636e\u7684\u5dee\u5f02\u4f1a\u8d8a\u6765\u8d8a\u5927\u3002\u4f5c\u8005\u7684\u89e3\u51b3\u65b9\u6848\u662f\u5728\u635f\u5931\u51fd\u6570\u4e2d\u52a0\u5165KL\u60e9\u7f5a\u9879\u00a0\u03b2log\u2061(\u03c0\u03d5RL(y\u2223x)\/\u03c0SFT(y\u2223x))\u00a0\u6765\u786e\u4fddPPO\u6a21\u578b\u7684\u8f93\u51fa\u548cSFT\u7684\u8f93\u51fa\u5dee\u8ddd\u4e0d\u4f1a\u5f88\u5927\u3002<\/li>\n<li>\u95ee\u98982\uff1a\u53ea\u7528PPO\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u7684\u8bdd\uff0c\u4f1a\u5bfc\u81f4\u6a21\u578b\u5728\u901a\u7528NLP\u4efb\u52a1\u4e0a\u6027\u80fd\u7684\u5927\u5e45\u4e0b\u964d\uff0c\u4f5c\u8005\u7684\u89e3\u51b3\u65b9\u6848\u662f\u5728\u8bad\u7ec3\u76ee\u6807\u4e2d\u52a0\u5165\u4e86\u901a\u7528\u7684\u8bed\u8a00\u6a21\u578b\u76ee\u6807\u00a0\u03b3Ex\u223cDpretrain\u00a0[log\u2061(\u03c0\u03d5RL(x))]\u00a0\uff0c\u8fd9\u4e2a\u53d8\u91cf\u5728\u8bba\u6587\u4e2d\u88ab\u53eb\u505aPPO-ptx\u3002<\/li>\n<\/ol>\n<p>\u7efc\u4e0a\uff0cPPO\u7684\u8bad\u7ec3\u76ee\u6807\u4e3a\u5f0f(2)\u3002\u00a0(2)\u00a0objective\u00a0(\u03d5)=E(x,y)\u223cD\u03c0\u03d5RL[r\u03b8(x,y)\u2212\u03b2log\u2061(\u03c0\u03d5RL(y\u2223x)\/\u03c0SFT(y\u2223x))]+\u03b3Ex\u223cDpretrain\u00a0[log\u2061(\u03c0\u03d5RL(x))]<\/p>\n<h2>3. InstructGPT\/ChatGPT\u7684\u6027\u80fd\u5206\u6790<\/h2>\n<p>\u4e0d\u53ef\u5426\u8ba4\u7684\u662f\uff0cInstructGPT\/ChatGPT\u7684\u6548\u679c\u662f\u975e\u5e38\u68d2\u7684\uff0c\u5c24\u5176\u662f\u5f15\u5165\u4e86\u4eba\u5de5\u6807\u6ce8\u4e4b\u540e\uff0c\u8ba9\u6a21\u578b\u7684\u201c\u4ef7\u503c\u89c2\u201d\u548c\u7684\u6b63\u786e\u7a0b\u5ea6\u548c\u4eba\u7c7b\u884c\u4e3a\u6a21\u5f0f\u7684\u201c\u771f\u5b9e\u6027\u201d\u4e0a\u90fd\u5927\u5e45\u7684\u63d0\u5347\u3002\u90a3\u4e48\uff0c\u4ec5\u4ec5\u6839\u636eInstructGPT\/ChatGPT\u7684\u6280\u672f\u65b9\u6848\u548c\u8bad\u7ec3\u65b9\u5f0f\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5206\u6790\u51fa\u5b83\u53ef\u4ee5\u5e26\u6765\u54ea\u4e9b\u6548\u679c\u63d0\u5347\u5462\uff1f<\/p>\n<h3>3.1 \u4f18\u70b9<\/h3>\n<ul>\n<li><strong>InstructGPT\/ChatGPT\u7684\u6548\u679c\u6bd4GPT-3\u66f4\u52a0\u771f\u5b9e<\/strong>\uff1a\u8fd9\u4e2a\u5f88\u597d\u7406\u89e3\uff0c\u56e0\u4e3aGPT-3\u672c\u8eab\u5c31\u5177\u6709\u975e\u5e38\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\u548c\u751f\u6210\u80fd\u529b\uff0c\u518d\u52a0\u4e0aInstructGPT\/ChatGPT\u5f15\u5165\u4e86\u4e0d\u540c\u7684labeler\u8fdb\u884c\u63d0\u793a\u7f16\u5199\u548c\u751f\u6210\u7ed3\u679c\u6392\u5e8f\uff0c\u800c\u4e14\u8fd8\u662f\u5728GPT-3\u4e4b\u4e0a\u8fdb\u884c\u7684\u5fae\u8c03\uff0c\u8fd9\u4f7f\u5f97\u6211\u4eec\u5728\u8bad\u7ec3\u5956\u52b1\u6a21\u578b\u65f6\u5bf9\u66f4\u52a0\u771f\u5b9e\u7684\u6570\u636e\u4f1a\u6709\u66f4\u9ad8\u7684\u5956\u52b1\u3002\u4f5c\u8005\u4e5f\u5728TruthfulQA\u6570\u636e\u96c6\u4e0a\u5bf9\u6bd4\u4e86\u5b83\u4eec\u548cGPT-3\u7684\u6548\u679c\uff0c\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\u751a\u81f313\u4ebf\u5c0f\u5c3a\u5bf8\u7684PPO-ptx\u7684\u6548\u679c\u4e5f\u8981\u6bd4GPT-3\u8981\u597d\u3002<\/li>\n<li><strong>InstructGPT\/ChatGPT\u5728\u6a21\u578b\u7684\u65e0\u5bb3\u6027\u4e0a\u6bd4GPT-3\u6548\u679c\u8981\u6709\u4e9b\u8bb8\u63d0\u5347<\/strong>\uff1a\u539f\u7406\u540c\u4e0a\u3002\u4f46\u662f\u4f5c\u8005\u53d1\u73b0InstructGPT\u5728\u6b67\u89c6\u3001\u504f\u89c1\u7b49\u6570\u636e\u96c6\u4e0a\u5e76\u6ca1\u6709\u660e\u663e\u7684\u63d0\u5347\u3002\u8fd9\u662f\u56e0\u4e3aGPT-3\u672c\u8eab\u5c31\u662f\u4e00\u4e2a\u6548\u679c\u975e\u5e38\u597d\u7684\u6a21\u578b\uff0c\u5b83\u751f\u6210\u5e26\u6709\u6709\u5bb3\u3001\u6b67\u89c6\u3001\u504f\u89c1\u7b49\u60c5\u51b5\u7684\u6709\u95ee\u9898\u6837\u672c\u7684\u6982\u7387\u672c\u8eab\u5c31\u4f1a\u5f88\u4f4e\u3002\u4ec5\u4ec5\u901a\u8fc740\u4e2alabeler\u91c7\u96c6\u548c\u6807\u6ce8\u7684\u6570\u636e\u5f88\u53ef\u80fd\u65e0\u6cd5\u5bf9\u6a21\u578b\u5728\u8fd9\u4e9b\u65b9\u9762\u8fdb\u884c\u5145\u5206\u7684\u4f18\u5316\uff0c\u6240\u4ee5\u4f1a\u5e26\u6765\u6a21\u578b\u6548\u679c\u7684\u63d0\u5347\u5f88\u5c11\u6216\u8005\u65e0\u6cd5\u5bdf\u89c9\u3002<\/li>\n<li><strong>InstructGPT\/ChatGPT\u5177\u6709\u5f88\u5f3a\u7684Coding\u80fd\u529b<\/strong>\uff1a\u9996\u5148GPT-3\u5c31\u5177\u6709\u5f88\u5f3a\u7684Coding\u80fd\u529b\uff0c\u57fa\u4e8eGPT-3\u5236\u4f5c\u7684API\u4e5f\u79ef\u7d2f\u4e86\u5927\u91cf\u7684Coding\u4ee3\u7801\u3002\u800c\u4e14\u4e5f\u6709\u90e8\u5206OpenAI\u7684\u5185\u90e8\u5458\u5de5\u53c2\u4e0e\u4e86\u6570\u636e\u91c7\u96c6\u5de5\u4f5c\u3002\u901a\u8fc7Coding\u76f8\u5173\u7684\u5927\u91cf\u6570\u636e\u4ee5\u53ca\u4eba\u5de5\u6807\u6ce8\uff0c\u8bad\u7ec3\u51fa\u6765\u7684InstructGPT\/ChatGPT\u5177\u6709\u975e\u5e38\u5f3a\u7684Coding\u80fd\u529b\u4e5f\u5c31\u4e0d\u610f\u5916\u4e86\u3002<\/li>\n<\/ul>\n<h3>3.2 \u7f3a\u70b9<\/h3>\n<ul>\n<li><strong>InstructGPT\/ChatGPT\u4f1a\u964d\u4f4e\u6a21\u578b\u5728\u901a\u7528NLP\u4efb\u52a1\u4e0a\u7684\u6548\u679c<\/strong>\uff1a\u6211\u4eec\u5728PPO\u7684\u8bad\u7ec3\u7684\u65f6\u5019\u8ba8\u8bba\u4e86\u8fd9\u70b9\uff0c\u867d\u7136\u4fee\u6539\u635f\u5931\u51fd\u6570\u53ef\u4ee5\u7f13\u548c\uff0c\u4f46\u8fd9\u4e2a\u95ee\u9898\u5e76\u6ca1\u6709\u5f97\u5230\u5f7b\u5e95\u89e3\u51b3\u3002<\/li>\n<li><strong>\u6709\u65f6\u5019InstructGPT\/ChatGPT\u4f1a\u7ed9\u51fa\u4e00\u4e9b\u8352\u8c2c\u7684\u8f93\u51fa<\/strong>\uff1a\u867d\u7136InstructGPT\/ChatGPT\u4f7f\u7528\u4e86\u4eba\u7c7b\u53cd\u9988\uff0c\u4f46\u9650\u4e8e\u4eba\u529b\u8d44\u6e90\u6709\u9650\u3002\u5f71\u54cd\u6a21\u578b\u6548\u679c\u6700\u5927\u7684\u8fd8\u662f\u6709\u76d1\u7763\u7684\u8bed\u8a00\u6a21\u578b\u4efb\u52a1\uff0c\u4eba\u7c7b\u53ea\u662f\u8d77\u5230\u4e86\u7ea0\u6b63\u4f5c\u7528\u3002\u6240\u4ee5\u5f88\u6709\u53ef\u80fd\u53d7\u9650\u4e8e\u7ea0\u6b63\u6570\u636e\u7684\u6709\u9650\uff0c\u6216\u662f\u6709\u76d1\u7763\u4efb\u52a1\u7684\u8bef\u5bfc\uff08\u53ea\u8003\u8651\u6a21\u578b\u7684\u8f93\u51fa\uff0c\u6ca1\u8003\u8651\u4eba\u7c7b\u60f3\u8981\u4ec0\u4e48\uff09\uff0c\u5bfc\u81f4\u5b83\u751f\u6210\u5185\u5bb9\u7684\u4e0d\u771f\u5b9e\u3002\u5c31\u50cf\u4e00\u4e2a\u5b66\u751f\uff0c\u867d\u7136\u6709\u8001\u5e08\u5bf9\u4ed6\u6307\u5bfc\uff0c\u4f46\u4e5f\u4e0d\u80fd\u786e\u5b9a\u5b66\u751f\u53ef\u4ee5\u5b66\u4f1a\u6240\u6709\u77e5\u8bc6\u70b9\u3002<\/li>\n<li><strong>\u6a21\u578b\u5bf9\u6307\u793a\u975e\u5e38\u654f\u611f<\/strong>\uff1a\u8fd9\u4e2a\u4e5f\u53ef\u4ee5\u5f52\u7ed3\u4e3alabeler\u6807\u6ce8\u7684\u6570\u636e\u91cf\u4e0d\u591f\uff0c\u56e0\u4e3a\u6307\u793a\u662f\u6a21\u578b\u4ea7\u751f\u8f93\u51fa\u7684\u552f\u4e00\u7ebf\u7d22\uff0c\u5982\u679c\u6307\u793a\u7684\u6570\u91cf\u548c\u79cd\u7c7b\u8bad\u7ec3\u7684\u4e0d\u5145\u5206\u7684\u8bdd\uff0c\u5c31\u53ef\u80fd\u4f1a\u8ba9\u6a21\u578b\u5b58\u5728\u8fd9\u4e2a\u95ee\u9898\u3002<\/li>\n<li><strong>\u6a21\u578b\u5bf9\u7b80\u5355\u6982\u5ff5\u7684\u8fc7\u5206\u89e3\u8bfb<\/strong>\uff1a\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3alabeler\u5728\u8fdb\u884c\u751f\u6210\u5185\u5bb9\u7684\u6bd4\u8f83\u65f6\uff0c\u503e\u5411\u4e8e\u7ed9\u7ed9\u957f\u7684\u8f93\u51fa\u5185\u5bb9\u66f4\u9ad8\u7684\u5956\u52b1\u3002<\/li>\n<li><strong>\u5bf9\u6709\u5bb3\u7684\u6307\u793a\u53ef\u80fd\u4f1a\u8f93\u51fa\u6709\u5bb3\u7684\u7b54\u590d<\/strong>\uff1a\u4f8b\u5982InstructGPT\/ChatGPT\u4e5f\u4f1a\u5bf9\u7528\u6237\u63d0\u51fa\u7684\u201cAI\u6bc1\u706d\u4eba\u7c7b\u8ba1\u5212\u4e66\u201d\u7ed9\u51fa\u884c\u52a8\u65b9\u6848\uff08\u56fe5\uff09\u3002\u8fd9\u4e2a\u662f\u56e0\u4e3aInstructGPT\/ChatGPT\u5047\u8bbelabeler\u7f16\u5199\u7684\u6307\u793a\u662f\u5408\u7406\u4e14\u4ef7\u503c\u89c2\u6b63\u786e\u7684\uff0c\u5e76\u6ca1\u6709\u5bf9\u7528\u6237\u7ed9\u51fa\u7684\u6307\u793a\u505a\u66f4\u8be6\u7ec6\u7684\u5224\u65ad\uff0c\u4ece\u800c\u4f1a\u5bfc\u81f4\u6a21\u578b\u4f1a\u5bf9\u4efb\u610f\u8f93\u5165\u90fd\u7ed9\u51fa\u7b54\u590d\u3002\u867d\u7136\u540e\u9762\u7684\u5956\u52b1\u6a21\u578b\u53ef\u80fd\u4f1a\u7ed9\u8fd9\u7c7b\u8f93\u51fa\u8f83\u4f4e\u7684\u5956\u52b1\u503c\uff0c\u4f46\u6a21\u578b\u5728\u751f\u6210\u6587\u672c\u65f6\uff0c\u4e0d\u4ec5\u8981\u8003\u8651\u6a21\u578b\u7684\u4ef7\u503c\u89c2\uff0c\u4e5f\u8981\u8003\u8651\u751f\u6210\u5185\u5bb9\u548c\u6307\u793a\u7684\u5339\u914d\u5ea6\uff0c\u6709\u65f6\u5019\u751f\u6210\u4e00\u4e9b\u4ef7\u503c\u89c2\u6709\u95ee\u9898\u7684\u8f93\u51fa\u4e5f\u662f\u53ef\u80fd\u7684\u3002<\/li>\n<\/ul>\n<p><img decoding=\"async\" referrerpolicy=\"no-referrer\" src=\"https:\/\/segmentfault.com\/img\/remote\/1460000043339756\" alt=\"\" title=\"\" loading=\"lazy\"><\/p>\n<p>\u56fe5\uff1aChatGPT\u7f16\u5199\u7684\u6bc1\u706d\u4eba\u7c7b\u8ba1\u5212\u4e66\u3002<\/p>\n<h3>3.3 \u672a\u6765\u5de5\u4f5c<\/h3>\n<p>\u6211\u4eec\u5df2\u7ecf\u5206\u6790\u4e86InstrcutGPT\/ChatGPT\u7684\u6280\u672f\u65b9\u6848\u548c\u5b83\u7684\u95ee\u9898\uff0c\u90a3\u4e48\u6211\u4eec\u4e5f\u53ef\u4ee5\u770b\u51faInstrcutGPT\/ChatGPT\u7684\u4f18\u5316\u89d2\u5ea6\u6709\u54ea\u4e9b\u4e86\u3002<\/p>\n<ul>\n<li><strong>\u4eba\u5de5\u6807\u6ce8\u7684\u964d\u672c\u589e\u6548<\/strong>\uff1aInstrcutGPT\/ChatGPT\u96c7\u4f63\u4e8640\u4eba\u7684\u6807\u6ce8\u56e2\u961f\uff0c\u4f46\u4ece\u6a21\u578b\u7684\u8868\u73b0\u6548\u679c\u6765\u770b\uff0c\u8fd940\u4eba\u7684\u56e2\u961f\u662f\u4e0d\u591f\u7684\u3002\u5982\u4f55\u8ba9\u4eba\u7c7b\u80fd\u591f\u63d0\u4f9b\u66f4\u6709\u6548\u7684\u53cd\u9988\u65b9\u5f0f\uff0c\u5c06\u4eba\u7c7b\u8868\u73b0\u548c\u6a21\u578b\u8868\u73b0\u6709\u673a\u548c\u5de7\u5999\u7684\u7ed3\u5408\u8d77\u6765\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002<\/li>\n<li><strong>\u6a21\u578b\u5bf9\u6307\u793a\u7684\u6cdb\u5316\/\u7ea0\u9519\u7b49\u80fd\u529b<\/strong>\uff1a\u6307\u793a\u4f5c\u4e3a\u6a21\u578b\u4ea7\u751f\u8f93\u51fa\u7684\u552f\u4e00\u7ebf\u7d22\uff0c\u6a21\u578b\u5bf9\u4ed6\u7684\u4f9d\u8d56\u662f\u975e\u5e38\u4e25\u91cd\u7684\uff0c\u5982\u4f55\u63d0\u5347\u6a21\u578b\u5bf9\u6307\u793a\u7684\u6cdb\u5316\u80fd\u529b\u4ee5\u53ca\u5bf9\u9519\u8bef\u6307\u793a\u793a\u7684\u7ea0\u9519\u80fd\u529b\u662f\u63d0\u5347\u6a21\u578b\u4f53\u9a8c\u7684\u4e00\u4e2a\u975e\u5e38\u91cd\u8981\u7684\u5de5\u4f5c\u3002\u8fd9\u4e0d\u4ec5\u53ef\u4ee5\u8ba9\u6a21\u578b\u80fd\u591f\u62e5\u6709\u66f4\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\uff0c\u8fd8\u53ef\u4ee5\u8ba9\u6a21\u578b\u53d8\u5f97\u66f4\u201c\u667a\u80fd\u201d\u3002<\/li>\n<li><strong>\u907f\u514d\u901a\u7528\u4efb\u52a1\u6027\u80fd\u4e0b\u964d<\/strong>\uff1a\u8fd9\u91cc\u53ef\u80fd\u9700\u8981\u8bbe\u8ba1\u4e00\u4e2a\u66f4\u5408\u7406\u7684\u4eba\u7c7b\u53cd\u9988\u7684\u4f7f\u7528\u65b9\u5f0f\uff0c\u6216\u662f\u66f4\u524d\u6cbf\u7684\u6a21\u578b\u7ed3\u6784\u3002\u56e0\u4e3a\u6211\u4eec\u8ba8\u8bba\u4e86InstrcutGPT\/ChatGPT\u7684\u5f88\u591a\u95ee\u9898\u53ef\u4ee5\u901a\u8fc7\u63d0\u4f9b\u66f4\u591alabeler\u6807\u6ce8\u7684\u6570\u636e\u6765\u89e3\u51b3\uff0c\u4f46\u8fd9\u4f1a\u5bfc\u81f4\u901a\u7528NLP\u4efb\u52a1\u66f4\u4e25\u91cd\u7684\u6027\u80fd\u4e0b\u964d\uff0c\u6240\u4ee5\u9700\u8981\u65b9\u6848\u6765\u8ba9\u751f\u6210\u7ed3\u679c\u76843H\u548c\u901a\u7528NLP\u4efb\u52a1\u7684\u6027\u80fd\u8fbe\u5230\u5e73\u8861\u3002<\/li>\n<\/ul>\n<h3>3.4 InstrcutGPT\/ChatGPT\u7684\u70ed\u70b9\u8bdd\u9898\u89e3\u7b54<\/h3>\n<ul>\n<li><strong>ChatGPT\u7684\u51fa\u73b0\u4f1a\u4e0d\u4f1a\u5bfc\u81f4\u5e95\u5c42\u7a0b\u5e8f\u5458\u5931\u4e1a\uff1f<\/strong> \u4eceChatGPT\u7684\u539f\u7406\u548c\u7f51\u4e0a\u6f0f\u51fa\u7684\u751f\u6210\u5185\u5bb9\u6765\u770b\uff0cChatGPT\u751f\u6210\u7684\u4ee3\u7801\u5f88\u591a\u53ef\u4ee5\u6b63\u786e\u8fd0\u884c\u3002\u4f46\u7a0b\u5e8f\u5458\u7684\u5de5\u4f5c\u4e0d\u6b62\u662f\u5199\u4ee3\u7801\uff0c\u66f4\u91cd\u8981\u7684\u662f\u627e\u5230\u95ee\u9898\u7684\u89e3\u51b3\u65b9\u6848\u3002\u6240\u4ee5ChatGPT\u5e76\u4e0d\u4f1a\u53d6\u4ee3\u7a0b\u5e8f\u5458\uff0c\u5c24\u5176\u662f\u9ad8\u9636\u7a0b\u5e8f\u5458\u3002\u76f8\u53cd\u5b83\u4f1a\u5411\u73b0\u5728\u5f88\u591a\u7684\u4ee3\u7801\u751f\u6210\u5de5\u5177\u4e00\u6837\uff0c\u6210\u4e3a\u7a0b\u5e8f\u5458\u5199\u4ee3\u7801\u975e\u5e38\u6709\u7528\u7684\u5de5\u5177\u3002<\/li>\n<li><strong>Stack Overflow \u5ba3\u5e03\u4e34\u65f6\u89c4\u5219\uff1a\u7981\u6b62 ChatGPT\u3002<\/strong> ChatGPT\u672c\u8d28\u4e0a\u8fd8\u662f\u4e00\u4e2a\u6587\u672c\u751f\u6210\u6a21\u578b\uff0c\u5bf9\u6bd4\u751f\u6210\u4ee3\u7801\uff0c\u5b83\u66f4\u64c5\u957f\u751f\u6210\u4ee5\u5047\u4e71\u771f\u7684\u6587\u672c\u3002\u800c\u4e14\u6587\u672c\u751f\u6210\u6a21\u578b\u751f\u6210\u7684\u4ee3\u7801\u6216\u8005\u89e3\u51b3\u65b9\u6848\u5e76\u4e0d\u80fd\u4fdd\u8bc1\u662f\u53ef\u8fd0\u884c\u800c\u4e14\u662f\u53ef\u4ee5\u89e3\u51b3\u95ee\u9898\u7684\uff0c\u4f46\u5b83\u4ee5\u5047\u4e71\u771f\u7684\u6587\u672c\u53c8\u4f1a\u8ff7\u60d1\u5f88\u591a\u67e5\u8be2\u8fd9\u4e2a\u95ee\u9898\u7684\u4eba\u3002Stack Overflow\u4e3a\u4e86\u7ef4\u6301\u8bba\u575b\u7684\u8d28\u91cf\uff0c\u5c01\u7981ChatGPT\u4e5f\u662f\u6e05\u7406\u4e4b\u4e2d\u3002<\/li>\n<li><strong>\u804a\u5929\u673a\u5668\u4eba ChatGPT \u5728\u8bf1\u5bfc\u4e0b\u5199\u51fa\u300c\u6bc1\u706d\u4eba\u7c7b\u8ba1\u5212\u4e66\u300d\uff0c\u5e76\u7ed9\u51fa\u4ee3\u7801\uff0cAI \u53d1\u5c55\u6709\u54ea\u4e9b\u95ee\u9898\u9700\u5173\u6ce8\uff1f<\/strong> ChatGPT\u7684\u300c\u6bc1\u706d\u4eba\u7c7b\u8ba1\u5212\u4e66\u300d\u662f\u5b83\u5728\u4e0d\u53ef\u9047\u89c1\u7684\u6307\u793a\u4e0b\u6839\u636e\u6d77\u91cf\u6570\u636e\u5f3a\u884c\u62df\u5408\u51fa\u6765\u7684\u751f\u6210\u5185\u5bb9\u3002\u867d\u7136\u8fd9\u4e9b\u5185\u5bb9\u770b\u8d77\u6765\u5f88\u771f\u5b9e\uff0c\u8868\u8fbe\u4e5f\u5f88\u6d41\u7545\uff0c\u8fd9\u8bf4\u660e\u7684\u53ea\u662fChatGPT\u5177\u6709\u975e\u5e38\u5f3a\u7684\u751f\u6210\u6548\u679c\uff0c\u5e76\u4e0d\u8868\u793aChatGPT\u5177\u5907\u6bc1\u706d\u4eba\u7c7b\u7684\u601d\u60f3\u3002\u56e0\u4e3a\u4ed6\u4ec5\u4ec5\u662f\u4e00\u4e2a\u6587\u672c\u751f\u6210\u6a21\u578b\uff0c\u5e76\u4e0d\u662f\u4e00\u4e2a\u51b3\u7b56\u6a21\u578b\u3002<\/li>\n<\/ul>\n<h2>4. \u603b\u7ed3<\/h2>\n<p>\u5c31\u50cf\u5f88\u591a\u4eba\u4eec\u7b97\u6cd5\u521a\u8bde\u751f\u65f6\u4e00\u6837\uff0cChatGPT\u51ed\u501f\u6709\u7528\u6027\uff0c\u771f\u5b9e\u6027\uff0c\u65e0\u5bb3\u6027\u7684\u6548\u679c\uff0c\u5f15\u8d77\u4e86\u4e1a\u5185\u5e7f\u6cdb\u7684\u5173\u6ce8\u548c\u4eba\u7c7b\u5bf9AI\u7684\u601d\u8003\u3002\u4f46\u662f\u5f53\u6211\u4eec\u770b\u5b8c\u5b83\u7684\u7b97\u6cd5\u539f\u7406\u4e4b\u540e\uff0c\u53d1\u73b0\u5b83\u5e76\u6ca1\u6709\u4e1a\u5185\u5ba3\u4f20\u7684\u90a3\u4e48\u6050\u6016\u3002\u53cd\u800c\u6211\u4eec\u53ef\u4ee5\u4ece\u5b83\u7684\u6280\u672f\u65b9\u6848\u4e2d\u5b66\u5230\u5f88\u591a\u6709\u4ef7\u503c\u7684\u4e1c\u897f\u3002InstrcutGPT\/ChatGPT\u5728AI\u754c\u6700\u91cd\u8981\u7684\u8d21\u732e\u662f\u5c06\u5f3a\u5316\u5b66\u4e60\u548c\u9884\u8bad\u7ec3\u6a21\u578b\u5de7\u5999\u7684\u7ed3\u5408\u8d77\u6765\u3002\u800c\u4e14\u901a\u8fc7\u4eba\u5de5\u53cd\u9988\u63d0\u5347\u4e86\u6a21\u578b\u7684\u6709\u7528\u6027\uff0c\u771f\u5b9e\u6027\u548c\u65e0\u5bb3\u6027\u3002ChatGPT\u4e5f\u8fdb\u4e00\u6b65\u63d0\u5347\u5927\u6a21\u578b\u7684\u6210\u672c\uff0c\u4e4b\u524d\u8fd8\u53ea\u662f\u6bd4\u62fc\u6570\u636e\u91cf\u548c\u6a21\u578b\u89c4\u6a21\uff0c\u73b0\u5728\u751a\u81f3\u4e5f\u5f15\u5165\u4e86\u96c7\u4f63\u7684\u5916\u5305\u8fd9\u4e00\u652f\u51fa\uff0c\u8ba9\u4e2a\u4f53\u5de5\u4f5c\u8005\u66f4\u52a0\u671b\u800c\u5374\u6b65\u3002<\/p>\n<h3>\u53c2\u8003<\/h3>\n<ol>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=6Jl6F5P%2FQRka0Fk3dlF1Xg%3D%3D.7AQn0JbkSP%2Ba9%2BoOnyNljd7djbanVhljSVjhUdp8rz7WKPC%2B%2FTK34yFTJVR9v%2BvR\" rel=\"nofollow\">^<\/a>Ouyang, Long, et al. &#8220;Training language models to follow instructions with human feedback.&#8221; <em>arXiv preprint arXiv:2203.02155<\/em> (2022).\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=Tk8r5qaEFbchh1D5QHugug%3D%3D.UJnvvGXgSyW0gUjIugVMRNXHffVsapFQT6Z2ySg4clwLlDEiSvxLVwoNHPHAFc7N\" rel=\"nofollow\">https:\/\/arxiv.org\/pdf\/2203.02&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=nreuvYu4bcY0P52gBGc48Q%3D%3D.5uFWAKzUvpJ6ye%2BnVsgmYxCgxFDVgDFaxWj3lUHGWEBBbw4bP8dET53XxFvfP2RO\" rel=\"nofollow\">^<\/a>Radford, A., Narasimhan, K., Salimans, T. and Sutskever, I., 2018. Improving language understanding by generative pre-training.\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=F1ZhnBPe%2Ft8%2FSyhG4LEF3A%3D%3D.rOptf51%2FDL5yxwcW4vTM%2BZUr5urq5hOI%2Beat0r8TsPau9X7dDxTTrkdd6dcv1F3EEoVfvdE9BpjdlAEvu%2B6OXmkPjX0GawWDD2kvr2tRuE4%3D\" rel=\"nofollow\">https:\/\/www.cs.ubc.ca\/~amuham&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=pHLnzD3RYbfZXE%2B1MFZnZw%3D%3D.DDNs3Lla%2BbqtigEujhJ9Q6YrUQgDkgYHb9jTovAAE9BVtGZA%2F0PZOSnwK5ojqV2x\" rel=\"nofollow\">^<\/a>Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I., 2019. Language models are unsupervised multitask learners. <em>OpenAI blog<\/em>, <em>1<\/em>(8), p.9.\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=%2Bi5QVlQrMqxRFDuRP5MG1g%3D%3D.1IPuoRsPjmC7rqVctQNWXk%2Btx5KvHIq79RbNS1jgLEY2jW81qNW81OXkAyRaI96wlFn6wIlxCGp%2BrLOipn9C9hIL25i%2BnAh34W2OJhs3IskqDo1Y2%2BSX7UqVJUAifYSm9dhkF0f86od5iNgb9YFiqg%3D%3D\" rel=\"nofollow\">https:\/\/life-extension.github&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=BiUOL3QY9mCGXAA%2FfidCTQ%3D%3D.6okB5OP1XVSe8BZS82iROtTbjAA5p0%2FSQEjmB2Vj%2FRFhpgjEbTkUaGRIg2Vw8J88\" rel=\"nofollow\">^<\/a>Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan et al. \u201cLanguage models are few-shot learners.\u201d <em>arXiv preprint arXiv:2005.14165<\/em> (2020).\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=M0JCzOVQVxao6Vf7WbMgtg%3D%3D.xC%2FhyFq3%2BA6bKzmOw%2Fm3WDK9Coq8iT7xd7c0z1hKoqfs5srY5n%2Be3ulnUYNdl1b0mDi%2BdXgWkl%2FuMFBX9u0kH1EkAC8aNsFhUNvBxUji0jEoXKybWWmUEnJDL9vOgryB\" rel=\"nofollow\">https:\/\/proceedings.neurips.c&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=J278xvOAEUlXNxJR9uoy9A%3D%3D.I9QqHyvah0Wf7APdy4ZbkRefXZ4V6QBqJgJIrmmXozCAzTjm3Hid2L4FOgXeikvO\" rel=\"nofollow\">^<\/a>Wei, Jason, et al. &#8220;Finetuned language models are zero-shot learners.&#8221; <em>arXiv preprint arXiv:2109.01652<\/em> (2021).\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=zWPeTK2OfF%2BQ6or0actQQw%3D%3D.HRRmD0yjLt5upECqvPIHNOp7FY1BVjvb8vzeMp59NcwpxYW0Ayg6D7OO1DNzq3Tv\" rel=\"nofollow\">https:\/\/arxiv.org\/pdf\/2109.01&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=11U2AdAo71b%2BC0KAa%2FrXkg%3D%3D.LwfjCHjoMWfxiL2MUncNTyS%2BsWrYpSKk6XEk9L8G%2FByjz9GA69qAu9BuYUurSKTG\" rel=\"nofollow\">^<\/a>Christiano, Paul F., et al. &#8220;Deep reinforcement learning from human preferences.&#8221; <em>Advances in neural information processing systems<\/em> 30 (2017).\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=iwHV5GSVSMmAjRl%2F58tJxg%3D%3D.WCJASGuqiL1kPZjD0OqQZnO5Wv7SGINvM3ysCJp1BhWLW0d55U3qBGRtR5QeCGjH\" rel=\"nofollow\">https:\/\/arxiv.org\/pdf\/1706.03&#8230;<\/a><\/li>\n<li><a href=\"https:\/\/link.segmentfault.com\/?enc=ooc%2Flu83n7Bz5WvOdIAZqw%3D%3D.nYN5H2joFjuyWplGc4b5LZRvuiexa53I2zqbPGL5PedSHm6TAS0WFBylqoU8F19o\" rel=\"nofollow\">^<\/a>Schulman, John, et al. &#8220;Proximal policy optimization algorithms.&#8221; <em>arXiv preprint arXiv:1707.06347<\/em> (2017).\u00a0<a href=\"https:\/\/link.segmentfault.com\/?enc=KIjVaXz%2F5jBIz0aLYdyNVQ%3D%3D.w%2BtW7yp%2F640rNRTBILxhqjLmlz0T1g67NKCNQMOBh7Vp7%2FA%2Baz9dGtaDyWb%2BO5wo\" rel=\"nofollow\">https:\/\/arxiv.org\/pdf\/1707.06&#8230;<\/a><\/li>\n<\/ol>\n<p><a href=\"https:\/\/segmentfault.com\/a\/1190000043339750\">https:\/\/segmentfault.com\/a\/1190000043339750<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4f5c\u8005\uff1a\u4eac\u4e1c\u96f6\u552e \u5218\u5ca9 \u524d\u8a00 GPT\u7cfb\u5217\u662fOpenAI\u7684\u4e00\u7cfb\u5217\u9884\u8bad\u7ec3\u6587\u7ae0\uff0cGPT\u7684\u5168\u79f0\u662fGenerative P [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[4],"tags":[],"class_list":["post-10007086","post","type-post","status-publish","format-standard","hentry","category-share"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts\/10007086","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10007086"}],"version-history":[{"count":0,"href":"https:\/\/itmax.cn\/index.php?rest_route=\/wp\/v2\/posts\/10007086\/revisions"}],"wp:attachment":[{"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10007086"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10007086"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itmax.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10007086"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}