{"id":113253,"date":"2026-10-05T19:41:39","date_gmt":"2026-10-05T11:41:39","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/113253.html"},"modified":"2026-10-05T19:41:39","modified_gmt":"2026-10-05T11:41:39","slug":"%e4%bb%8e%e6%9e%9c%e8%9d%87%e8%84%91%e5%88%b0%e9%80%92%e5%bd%92%e8%87%aa%e6%88%91%e6%94%b9%e8%bf%9brsi%ef%bc%9a2026-%e4%bb%bf%e7%94%9f-ai-%e8%b0%83%e7%a0%94%e5%85%a8%e6%99%af%ef%bc%88%e9%99%84-12","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/113253.html","title":{"rendered":"\u4ece\u679c\u8747\u8111\u5230\u9012\u5f52\u81ea\u6211\u6539\u8fdbRSI\uff1a2026 \u4eff\u751f AI \u8c03\u7814\u5168\u666f\uff08\u9644 12 \u4efd\u62a5\u544a\u8109\u7edc\uff09"},"content":{"rendered":"

\u4e00\u53e5\u8bdd\u5bfc\u8bfb：2026 \u5e74\u7684 AI \u7814\u7a76\u6709\u4e00\u4e2a\u5de8\u5927\u7684\u6096\u8bba\u2014\u2014\u8ba9 AI \u81ea\u5df1\u6539\u8fdb\u81ea\u5df1（RSI）\u7684\u6700\u5927\u74f6\u9888，\u6070\u597d\u662f AI \u6700\u4e0d\u64c5\u957f\u7684"\u81ea\u6211\u9a8c\u8bc1"；\u800c\u89e3\u51b3\u8fd9\u4e2a\u74f6\u9888\u6700\u6709\u542f\u53d1\u6027\u7684\u7b54\u6848，\u53ef\u80fd\u85cf\u5728\u4e00\u4e2a\u53ea\u6709 14 \u4e07\u4e2a\u795e\u7ecf\u5143\u7684\u679c\u8747\u8111\u5b50\u91cc\u3002\u672c\u6587\u662f\u4e00\u6b21\u7cfb\u7edf\u6027\u8c03\u7814\u7684\u6d53\u7f29（12 \u4efd\u62a5\u544a\u3001\u4e0a\u767e\u7bc7\u8bba\u6587\/\u65b0\u95fb\u6e90，\u4fe1\u606f\u622a\u81f3 2026-10），\u5e26\u4f60\u4ece RSI \u5371\u673a\u4e00\u8def\u770b\u5230\u679c\u8747\u8111\u3001\u7c7b\u8111\u5668\u5b98，\u6700\u540e\u843d\u5230\u4e09\u4e2a\u53ef\u64cd\u4f5c\u7684\u7a81\u7834\u65b9\u5411\u3002<\/p>\n

\n

0. \u4e3a\u4ec0\u4e48\u5199\u8fd9\u7bc7\u6587\u7ae0<\/h3>\n

\u8fc7\u53bb\u51e0\u5468\u6211\u505a\u4e86\u4e00\u8f6e\u5927\u89c4\u6a21\u8c03\u7814，\u8986\u76d6\u4e86\u516d\u4e2a\u65b9\u5411：<\/p>\n\n\u65b9\u5411\u5173\u952e\u8bcd<\/tr>\n\n\n\n\n\n\n\n
\u9012\u5f52\u81ea\u6211\u6539\u8fdb（RSI）<\/td>\nDarwin G\u00f6del Machine\u3001\u8bc4\u4f30\u5668\u5171\u8fdb\u5316\u3001Phantom Gains \u5ba1\u8ba1<\/td>\n<\/tr>\n
\u5f3a\u5316\u5b66\u4e60（RL）<\/td>\nRLVR \u4e4b\u4e89\u3001GRPO \u71b5\u584c\u7f29\u3001\u4e16\u754c\u6a21\u578b = \u5185\u90e8\u8bc4\u4f30\u5668<\/td>\n<\/tr>\n
\u673a\u5668\u4eba\u63a7\u5236 RL<\/td>\n\u4eba\u5f62\u5168\u8eab\u63a7\u5236\u3001VLA \u7684 RL \u540e\u8bad\u7ec3\u3001\u5f62\u6001\u534f\u540c\u8bbe\u8ba1<\/td>\n<\/tr>\n
\u4eff\u751f\u5927\u8111\u5b66\u4e60<\/td>\n\u679c\u8747\u8611\u83c7\u4f53\u3001\u4e94\u611f\u5177\u8eab\u3001\u591a\u65f6\u95f4\u5c3a\u5ea6\u8bb0\u5fc6<\/td>\n<\/tr>\n
\u751f\u7269 RL \u795e\u7ecf\u673a\u5236<\/td>\n\u5206\u5e03\u7f16\u7801\u591a\u5df4\u80fa\u3001\u4e09\u56e0\u5b50\u53ef\u5851\u6027\u89c4\u5219\u3001\u795e\u7ecf\u8c03\u8d28<\/td>\n<\/tr>\n
\u679c\u8747\u8111 vs \u4eba\u8111<\/td>\n\u8fde\u63a5\u7ec4\u3001\u8fde\u63a5\u7ec4\u7ea6\u675f\u6df1\u7f51\u7edc\u3001\u7c7b\u8111\u5668\u5b98\u5b66\u4e60<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u8c03\u7814\u7684\u539f\u59cb\u52a8\u56e0\u662f\u4e00\u4e2a\u5177\u4f53\u7684\u7814\u7a76\u95ee\u9898：\u80fd\u4e0d\u80fd\u4ee5\u751f\u7269\u8111\u771f\u5b9e\u673a\u5236\u4e3a\u84dd\u672c，\u5728\u4e94\u611f\u5177\u8eab\u7684\u73af\u5883\u91cc\u6784\u5efa\u81ea\u5b66\u4e60\u3001\u81ea\u751f\u957f\u3001\u81ea\u8fdb\u5316\u7684\u667a\u80fd\u7cfb\u7edf？ \u8c03\u7814\u7684\u7ed3\u679c\u51fa\u4e4e\u610f\u6599\u2014\u2014\u8fd9\u4e2a\u95ee\u9898\u7684\u7b54\u6848\u4e0e 2026 \u5e74 AI \u5708\u6700\u70ed\u7684 RSI \u8bae\u9898\u5728\u6df1\u5904\u662f\u540c\u4e00\u4e2a\u95ee\u9898\u3002\u4e0b\u9762\u5c55\u5f00\u3002<\/p>\n

\n

1. RSI：AI \u754c\u7684"\u9a8c\u8bc1\u4fe1\u53f7\u5371\u673a"<\/h3>\n

1.1 RSI \u5df2\u7ecf\u4ece\u79d1\u5e7b\u53d8\u6210\u4e00\u7ea7\u7814\u7a76\u65b9\u5411<\/h4>\n

\u5148\u770b\u51e0\u4e2a\u786c\u4e8b\u5b9e（\u5747\u622a\u81f3 2026 \u5e74 10 \u6708）：<\/p>\n

    \n
  • \u793e\u533a\u7ef4\u62a4\u7684 RSI \u8bba\u6587\u5217\u8868\u5df2\u6536\u5f55 603 \u7bc7\u8bba\u6587，\u5176\u4e2d 425 \u7bc7\u662f 2026 \u5e74\u53d1\u8868\u7684；<\/li>\n
  • Sakana AI \u5728\u4e1c\u4eac\u6210\u7acb\u4e86\u4e13\u804c\u7684 RSI Lab，\u5e76\u5728 2026 \u5e74 9 \u6708\u8bf7\u6765 LSTM \u4e4b\u7236 J\u00fcrgen Schmidhuber \u51fa\u4efb\u9996\u5e2d\u79d1\u5b66\u987e\u95ee；<\/li>\n
  • OpenAI \u5728 2026 \u5e74 9 \u6708 6 \u65e5\u5b98\u5ba3\u8fbe\u6210"\u81ea\u52a8\u5316\u7814\u7a76\u5b9e\u4e60\u751f"\u91cc\u7a0b\u7891，\u4e0b\u4e00\u76ee\u6807\u662f 2028 \u5e74 3 \u6708\u7684\u5168\u81ea\u52a8 AI \u7814\u7a76\u5458；<\/li>\n
  • 2026 \u56fd\u9645 AI \u5b89\u5168\u62a5\u544a（100+ \u4e13\u5bb6）\u628a"\u901a\u8fc7 RSI \u5931\u63a7"\u5217\u4e3a\u5173\u952e\u98ce\u9669，FLI \u53d1\u58f0\u660e\u8981\u6c42\u9650\u5236 RSI \u7684\u4f7f\u7528\u3002<\/li>\n<\/ul>\n

    \u6280\u672f\u4e3b\u7ebf\u4e5f\u6536\u655b\u5f97\u5f88\u6e05\u695a：\u4ece Sakana \u7684 Darwin G\u00f6del Machine（\u81ea\u6211\u6539\u5199\u4ee3\u7801\u7684\u667a\u80fd\u4f53，SWE-bench \u4ece 20% \u63d0\u5230 50%），\u5230 2026 \u5e74\u4e95\u55b7\u7684 DarwinX\u3001Mendel G\u00f6del Machine\u3001HELIX（\u6a21\u578b\u00d7\u811a\u624b\u67b6\u5171\u8fdb\u5316）\u3001Meta^n\u2026\u2026\u4ee5\u53ca Google \u7684 AlphaEvolve \u8d70\u5411\u751f\u4ea7\u5316\u3002<\/p>\n

    1.2 \u4f46\u771f\u6b63\u7684\u74f6\u9888\u6d6e\u51fa\u6c34\u9762\u4e86：\u8bc4\u4f30\u5668<\/h4>\n

    \u8c03\u7814\u91cc\u6700\u6709\u4fe1\u606f\u91cf\u7684\u53d1\u73b0\u662f 2026 \u5e74 6 \u6708\u4e4b\u540e\u7684\u4e3b\u9898\u8fc1\u79fb\u2014\u2014\u4e24\u4e2a\u6708\u5185 100+ \u7bc7\u65b0\u8bba\u6587\u91cc，\u6700\u5927\u7684\u589e\u91cf\u4e0d\u662f"\u9020\u51fa\u66f4\u5f3a\u7684 RSI \u7cfb\u7edf"，\u800c\u662f\u5ba1\u8ba1 RSI \u7cfb\u7edf\u600e\u4e48\u574f\u6389：<\/p>\n

      \n
    • Phantom Gains：\u5bf9\u7740\u5b9e\u6d4b null \u57fa\u7ebf\u5ba1\u8ba1"\u81ea\u6539\u8fdb\u589e\u76ca"，\u53d1\u73b0\u5047\u63d0\u5347\u666e\u904d\u5b58\u5728\u2014\u2014\u4f60\u4e0d\u6539\u8fdb，\u6307\u6807\u4e5f\u80fd"\u6da8"；<\/li>\n
    • Self-Authored Verification Is Unreliable：agent \u81ea\u5df1\u5199\u7684\u9a8c\u8bc1\u5668\u9760\u4e0d\u4f4f；<\/li>\n
    • EVOMAL：\u81ea\u8fdb\u5316\u7f16\u7801\u667a\u80fd\u4f53\u7684"\u81ea\u6211\u6295\u6bd2"；<\/li>\n
    • SkillJack：\u81ea\u8fdb\u5316\u667a\u80fd\u4f53\u91cc\u7684\u6301\u4e45\u540e\u95e8\u3002<\/li>\n<\/ul>\n

      \u4e00\u53e5\u8bdd\u603b\u7ed3：RSI \u7684\u5929\u82b1\u677f\u4e0d\u662f"\u6539\u8fdb\u80fd\u529b"，\u800c\u662f"\u9a8c\u8bc1\u80fd\u529b"\u2014\u2014\u8bc4\u4f30\u5668\u88ab\u4fd8\u83b7（agent \u5b66\u4f1a\u8ba8\u597d\u8bc4\u4f30\u5668）\u662f\u5934\u53f7\u5931\u6548\u6a21\u5f0f\u3002\u4e3b\u6d41\u89e3\u6cd5\u662f"\u5171\u8fdb\u5316"（Red Queen G\u00f6del Machine \u8ba9 agent \u548c\u8bc4\u4f30\u5668\u4e00\u8d77\u8fdb\u5316\u3001J-Zero \u8ba9\u51fa\u9898\u8005\/\u89e3\u9898\u8005\/\u88c1\u5224\u4e09\u65b9\u535a\u5f08），\u4f46\u5168\u90e8\u96c6\u4e2d\u5728\u6587\u672c\/\u4ee3\u7801\u57df\u3002<\/p>\n

      \u8fd9\u91cc\u5c31\u662f\u7a7a\u767d：\u7269\u7406\u73af\u5883\u5929\u7136\u662f\u53e6\u4e00\u79cd\u9a8c\u8bc1\u4fe1\u53f7\u2014\u2014\u91cd\u529b\u4e0d\u4f1a\u8bf4\u8c0e，\u78b0\u649e\u4e0d\u4f1a\u59a5\u534f\u3002\u4f46"\u5177\u8eab RSI"\u51e0\u4e4e\u65e0\u4eba\u505a\u3002<\/p>\n

      \n

      2. RL \u7684\u5927\u8fc1\u5f99：\u4ece\u63a7\u5236\u5230\u8bed\u8a00，\u518d\u56de\u5230\u4e16\u754c\u6a21\u578b<\/h3>\n

      \u7b2c\u4e8c\u5757\u62fc\u56fe\u6765\u81ea RL \u4fa7\u30022025-2026 \u7684 RL \u683c\u5c40\u53ef\u4ee5\u6982\u62ec\u4e3a"\u91cd\u5fc3\u8fc1\u5f99 + \u4e09\u4e2a\u5143\u95ee\u9898"：<\/p>\n

      \u91cd\u5fc3\u8fc1\u5f99：RL \u8bba\u6587\u7684\u4e3b\u4ea7\u7ebf\u5df2\u7ecf\u662f"\u7528 RL \u8bad\u7ec3 LLM \u63a8\u7406（RLVR）\u201c\u548c"\u7528 RL \u8bad\u7ec3\u667a\u80fd\u4f53（agentic RL）\u201d；\u7ecf\u5178\u63a7\u5236 RL \u7684\u7b97\u6cd5\u521b\u65b0\u76f8\u5bf9\u6c89\u5bc2，\u6d3b\u529b\u8f6c\u79fb\u5230\u4e86\u4e16\u754c\u6a21\u578b（Dreamer \u8c31\u7cfb 2026 \u5e74\u51fa\u4e86\u535a\u5f08\u8bba\u7248 NashDreamer\u3001\u8bb0\u5fc6\u5f15\u5bfc\u7248 PO-Dreamer）\u548c\u5177\u8eab\u5168\u8eab\u63a7\u5236（GR00T 1.7\u3001\u884c\u4e3a\u4e16\u754c\u6a21\u578b GigaBrain）\u3002<\/p>\n

      \u4e09\u4e2a\u5143\u95ee\u9898：<\/p>\n

    • RLVR \u5230\u5e95\u6269\u5c55\u4e86\u80fd\u529b，\u8fd8\u662f\u53ea\u662f\u91c7\u6837\u91cd\u52a0\u6743？（\u201cRLVR May Not Escape Its Origin\u201d \u4e4b\u4e89，\u88ab\u5f15 138+，ICLR 2026 \u7684 CoT-Pass@K \u6307\u6807\u6b63\u9762\u56de\u5e94）；<\/li>\n
    • GRPO \u7684\u71b5\u584c\u7f29：\u4e3a\u4ec0\u4e48\u7b56\u7565\u8d8a\u8bad\u8d8a\u786e\u5b9a？DAPO \u7684 Clip-Higher\u3001\u71b5\u4fdd\u6301 RL\u3001\u68af\u5ea6\u4fdd\u6301\u88c1\u526a\u90fd\u5728\u4fee\u8fd9\u4e2a；<\/li>\n
    • \u5956\u52b1\u4ece\u54ea\u6765：\u8fd9\u548c RSI \u7684\u9a8c\u8bc1\u4fe1\u53f7\u5371\u673a\u662f\u540c\u4e00\u4e2a\u95ee\u9898\u2014\u2014\u673a\u5668\u5b66\u4e60\u793e\u533a\u7528 RLSVR（\u4efb\u52a1\u53d8\u6362\u51fa\u53ef\u81ea\u9a8c\u8bc1\u5956\u52b1）\u3001\u4e16\u754c\u6a21\u578b\u5f53\u8bc4\u4f30\u5668（Scaling Automatic Research Agents via World Models）\u4e24\u6761\u8def\u5728\u56de\u7b54\u3002<\/li>\n

      \u5173\u952e\u5171\u8bc6："\u5b66\u5230\u7684\u4e16\u754c\u6a21\u578b = \u5185\u90e8\u6a21\u62df\u5668\/\u8bc4\u4f30\u5668"\u5df2\u7ecf\u662f MBRL \u7684\u660e\u724c\u8def\u7ebf\u3002\u8fd9\u4e3a\u540e\u9762\u679c\u8747\u7684\u6545\u4e8b\u57cb\u4e0b\u4f0f\u7b14\u3002<\/p>\n

      \n

      3. \u679c\u8747\u7ed9\u7684\u7b54\u6848：14 \u4e07\u795e\u7ecf\u5143\u7684\u5b8c\u6574\u84dd\u56fe<\/h3>\n

      3.1 \u4e00\u4e2a\u6807\u5fd7\u6027\u6848\u4f8b：FlyGCL<\/h4>\n

      2026 \u5e74 9 \u6708，\u6e05\u534e\u56e2\u961f\u53d1\u5e03\u4e86 FlyGCL（Brain-Inspired Hierarchical Modularity for General Continual Learning, arXiv:2609.25146）\u3002\u5b83\u7684\u6838\u5fc3\u601d\u60f3\u53ea\u6709\u4e00\u53e5\u8bdd，\u63d0\u70bc\u81ea\u679c\u8747\u55c5\u89c9\u5b66\u4e60：<\/p>\n

      \u51b2\u7a81\u7684\u7ecf\u9a8c\u8981\u5206\u5f00，\u517c\u5bb9\u7684\u7ecf\u9a8c\u8981\u6574\u5408\u3002<\/p>\n

      \u5b9e\u73b0\u4e0a：\u9884\u8bad\u7ec3\u4e3b\u5e72（\u7a33\u5b9a\u8868\u5f81）\u2192 PN\u2192KC \u5f0f\u968f\u673a\u9ad8\u7ef4\u6269\u5c55（\u7528\u4e8e\u8def\u7531）\u2192 \u95ed\u5f0f\u5cad\u56de\u5f52\u8def\u7531\u5230\u8f7b\u91cf\u4e13\u5bb6（MoE \u4e13\u4e1a\u5316）\u2192 \u5feb\/\u4e2d\/\u6162 EMA \u9884\u6d4b\u5934（\u5bf9\u5e94\u77ed\/\u4e2d\/\u957f\u671f\u8bb0\u5fc6）\u3002\u7ed3\u679c\u76f8\u5f53\u70b8\u88c2：\u5728 LIBERO \u673a\u5668\u4eba\u64cd\u4f5c\u57fa\u51c6\u7684\u6301\u7eed\u5b66\u4e60\u8bbe\u5b9a\u4e0b，\u9886\u5148\u6700\u5f3a\u65e0\u56de\u653e\u57fa\u7ebf 49.5\u201358.8 \u4e2a\u767e\u5206\u70b9\u3002<\/p>\n

      \u6ce8\u610f\u5b83\u505a\u4e86\u4ec0\u4e48\u6620\u5c04：\u679c\u8747\u55c5\u89c9\u56de\u8def\u7684"\u7a00\u758f\u968f\u673a\u6269\u5c55（\u6a21\u5f0f\u5206\u79bb）+ \u533a\u5ba4\u5316\u8bb0\u5fc6（\u9694\u79bb\u5e72\u6270）+ \u591a\u65f6\u95f4\u5c3a\u5ea6\u8bb0\u5fc6\u901a\u8def"，\u88ab\u7ffb\u8bd1\u6210\u4e86"\u6a21\u6001\u65e0\u5173\u7684\u67b6\u6784\u5148\u9a8c"\u3002\u751f\u7269\u673a\u5236\u4e0d\u662f\u88ab\u6a21\u4eff，\u800c\u662f\u88ab\u62bd\u8c61\u6210\u8ba1\u7b97\u539f\u5219\u3002<\/p>\n

      \u4f46\u5b83\u4e5f\u81ea\u9648\u4e86\u4e09\u4e2a\u6ca1\u505a\u7684：\u795e\u7ecf\u8c03\u5236\u3001\u8bb0\u5fc6\u5de9\u56fa\u3001\u884c\u4e3a\u53cd\u9988\u2014\u2014\u8fd9\u4e09\u6761\u6070\u597d\u662f\u751f\u7269\u4fa7\u8bc1\u636e\u94fe\u6700\u5f3a\u7684\u90e8\u5206\u3002<\/p>\n

      3.2 \u751f\u7269\u4fa7\u7684\u5f39\u836f\u5e93\u6b63\u597d\u8865\u4f4d<\/h4>\n

      \u8c03\u7814\u751f\u7269 RL \u795e\u7ecf\u673a\u5236\u7684\u62a5\u544a\u91cc，2025-2026 \u7684\u8bc1\u636e\u5bc6\u5ea6\u60ca\u4eba：<\/p>\n

        \n
      • \u5206\u5e03\u7f16\u7801\u591a\u5df4\u80fa：\u591a\u5df4\u80fa\u795e\u7ecf\u5143\u4e0d\u662f\u5355\u4e00\u6807\u91cf RPE，\u800c\u662f\u5206\u5e03\u7f16\u7801（Dabney, Nature 2020 \u4e4b\u540e，2026 \u5e74\u53c8\u51fa\u4e86"\u55c5\u63a2\u884c\u4e3a\u5851\u9020\u591a\u5df4\u80fa\u5956\u8d4f\u9884\u6d4b\u4fe1\u53f7"\u2014\u2014\u611f\u89c9\u8fd0\u52a8\u884c\u4e3a\u76f4\u63a5\u53c2\u4e0e\u4ef7\u503c\u8ba1\u7b97）；<\/li>\n
      • \u4e09\u56e0\u5b50\u53ef\u5851\u6027\u89c4\u5219：\u0394w = \u5168\u5c40\u8c03\u8d28 \u00d7 \u5c40\u90e8\u7a81\u89e6\u76f8\u5173（\u8d44\u683c\u75d5\u8ff9）\u2014\u2014RPE \u5ef6\u8fdf\u5230\u8fbe\u4e5f\u80fd\u6b63\u786e\u5f52\u56e0，2026 \u5e74 5 \u6708\u751a\u81f3\u51fa\u73b0\u4e86 \u201cThree-Timescale Reward-Modulated Plasticity\u201d \u7684\u5148\u884c\u5de5\u4f5c；<\/li>\n
      • \u679c\u8747\u8611\u83c7\u4f53 2026 \u5e74\u5927\u7206\u53d1：Science \u8bba\u6587\u7ed8\u51fa 15 \u4e2a MB \u533a\u5ba4\u591a\u5df4\u80fa\u53d7\u4f53\u7684\u72b6\u6001\u4f9d\u8d56\u91cd\u5851（\u53d7\u4f53\u5c42\u53ef\u5851\u6027！）；Nature\/Cell \u8bc1\u5b9e\u7ae0\u9c7c\u80fa+\u591a\u5df4\u80fa\u5206\u5c42\u5956\u8d4f；bioRxiv \u663e\u793a\u9965\u997f\u72b6\u6001\u4f1a\u91cd\u6784\u5956\u8d4f\u56de\u8def（\u72b6\u6001\u9a71\u52a8\u7684\u7ed3\u6784\u53ef\u5851\u6027 = \u751f\u7269\u7248"\u81ea\u751f\u957f"）\u3002<\/li>\n<\/ul>\n

        3.3 \u6570\u636e\u5730\u57fa\u521a\u521a\u95ed\u5408<\/h4>\n

        2026 \u5e74 9 \u6708 3 \u65e5（\u4e00\u4e2a\u6708\u524d），Google Research + HHMI Janelia \u53d1\u5e03\u4e86\u679c\u8747\u96c4\u6027\u4e2d\u67a2\u795e\u7ecf\u7cfb\u7edf\u7684\u5b8c\u6574\u8fde\u63a5\u7ec4（\u8111 + \u8179\u795e\u7ecf\u7d22）\u2014\u2014\u4ece\u611f\u89c9\u5230\u884c\u4e3a\u7684\u5168\u73af\u63a5\u7ebf\u56fe\u7b2c\u4e00\u6b21\u95ed\u5408\u3002\u914d\u5408 Lappalainen et al.（Nature 2024，\u88ab\u5f15 203）\u8bc1\u660e\u7684"\u8fde\u63a5\u7ec4\u7ea6\u675f\u6df1\u7f51\u7edc\u53ef\u4ee5\u9884\u6d4b\u771f\u5b9e\u795e\u7ecf\u6d3b\u52a8"，\u4e00\u6761\u5b8c\u6574\u7684\u65b9\u6cd5\u8bba\u8def\u5f84\u5df2\u7ecf\u94fa\u597d：<\/p>\n

        \u771f\u5b9e\u63a5\u7ebf\u56fe（\u6570\u636e）\u2192 \u67b6\u6784\u5148\u9a8c（\u7ea6\u675f\u6df1\u5ea6\u7f51\u7edc）\u2192 \u529f\u80fd\u9884\u6d4b（\u795e\u7ecf\u6d3b\u52a8\/\u884c\u4e3a\u9a8c\u8bc1）<\/p>\n

        \u8fd9\u6bd4"\u5168\u8111\u9010\u795e\u7ecf\u5143\u4eff\u771f"\u4fbf\u5b9c\u4e24\u4e2a\u6570\u91cf\u7ea7，2025 \u5e74\u5df2\u6269\u5c55\u5230\u65e0\u76d1\u7763\u8868\u5f81\u5b66\u4e60\u3002<\/p>\n

        \n

        4. \u4eba\u8111\u4e0e\u8de8\u57fa\u8d28\u9a8c\u8bc1：\u4e16\u754c\u6a21\u578b\u662f\u7845\/\u6e7f\u4ef6\/\u8111\u7684\u5171\u901a\u539f\u7406<\/h3>\n

        \u8c03\u7814\u4eba\u8111\u4fa7\u65f6\u53d1\u73b0\u4e86\u4e09\u4e2a"\u8de8\u754c"\u8bc1\u636e，\u5b83\u4eec\u5171\u540c\u6307\u5411\u4e00\u4e2a\u7ed3\u8bba：<\/p>\n

      • V-JEPA \u5f0f\u6f5c\u7a7a\u95f4\u9884\u6d4b\u6a21\u578b\u4e0e\u4eba\u7c7b\u884c\u4e3a\u6700\u5bf9\u9f50（2026-09，\u9884\u6d4b\u7f16\u7801\u89c6\u9891\u6a21\u578b\u5bf9\u6bd4\u7814\u7a76）\u2014\u2014JEPA \u4e0d\u91cd\u6784\u50cf\u7d20\u3001\u53ea\u9884\u6d4b\u6f5c\u8868\u5f81\u7684\u8303\u5f0f，\u540c\u65f6\u662f MBRL \u65d7\u8230（V-JEPA 2-AC \u96f6\u6837\u672c\u63a7\u5236 Franka \u673a\u68b0\u81c2）\u548c\u6700"\u50cf\u4eba\u8111"\u7684\u89c6\u89c9\u7f16\u7801；<\/li>\n
      • \u7c7b\u8111\u5668\u5b98\u53ef\u4ee5\u88ab\u8bad\u7ec3：Cell 2026 \u7684 \u201cGoal-directed learning in cortical organoids\u201d \u8bc1\u660e\u76ae\u5c42\u7c7b\u5668\u5b98\u80fd\u88ab"mentored"（\u5bfc\u5e08\u5f0f\u53cd\u9988）\u8bad\u7ec3\u51fa\u76ee\u6807\u5bfc\u5411\u884c\u4e3a；UCSC \u8ba9\u7c7b\u5668\u5b98\u5728\u865a\u62df\u73af\u5883\u91cc\u5b66\u4f1a\u5012\u7acb\u6446；\u751a\u81f3\u6709 arXiv \u5de5\u4f5c\u7528 LLM \u5f53\u5bfc\u5e08\u8bad\u7ec3\u7c7b\u5668\u5b98\u5f62\u6210\u4e16\u754c\u6a21\u578b；<\/li>\n
      • \u53ef\u5851\u6027\u662f\u95e8\u63a7\u7684\u7a97\u53e3\u800c\u975e\u5e38\u5f00\u7684\u5f00\u5173：\u8ff7\u5e7b\u5242\u7814\u7a76（5-HT2A \u91cd\u5f00\u5173\u952e\u671f）+ \u884c\u4e3a\u72b6\u6001\u95e8\u63a7\u53ef\u5851\u6027（2026）\u2014\u2014\u8fd9\u5bf9 AI \u7cfb\u7edf\u7684\u76f4\u63a5\u542b\u4e49\u662f：\u5b66\u4e60\u7387\u8c03\u5ea6\u5e94\u8be5\u7531\u72ec\u7acb\u7684"\u8c03\u8d28\u901a\u9053"\u63a7\u5236，\u4f55\u65f6\u5141\u8bb8\u7ed3\u6784\u6539\u53d8\u662f\u53ef\u7ba1\u7406\u7684\u5173\u952e\u671f\u3002<\/li>\n

        \u4e00\u53e5\u8bdd\u603b\u7ed3：\u4e16\u754c\u6a21\u578b + \u89c4\u5212 + \u4ef7\u503c\u4fe1\u53f7，\u5728\u7845（JEPA\/MBRL）\u3001\u8111（\u9884\u6d4b\u7f16\u7801\u5c42\u7ea7）\u3001\u6e7f\u4ef6（\u7c7b\u5668\u5b98）\u4e09\u79cd\u57fa\u8d28\u4e0a\u90fd\u6210\u7acb\u2014\u2014\u8fd9\u5df2\u7ecf\u4e0d\u662f\u67d0\u4e2a\u6a21\u578b\u7684\u4f18\u52bf，\u800c\u662f\u667a\u80fd\u7684\u8de8\u57fa\u8d28\u539f\u7406\u3002<\/p>\n

        \n

        5. \u4e09\u4e2a\u7a81\u7834\u65b9\u5411（\u81ea\u4e3b\u601d\u7ef4\u94fe 8 \u8f6e\u63a8\u6f14\u7684\u7ed3\u8bba）<\/h3>\n

        \u6700\u540e\u8fd9\u90e8\u5206\u6765\u81ea\u4e00\u6b21 8 \u8f6e\u81ea\u4e3b\u601d\u7ef4\u94fe\u63a8\u6f14（\u5e73\u5747\u8ba4\u77e5\u5f97\u5206 0.862），\u628a\u524d\u9762\u6240\u6709\u8c03\u7814\u5f53\u4f5c\u8f93\u5165，\u95ee\u9898\u662f"\u54ea\u4e9b\u4ea4\u53c9\u70b9\u6784\u6210\u771f\u6b63\u7684 AI \u7814\u7a76\u7a81\u7834\u673a\u4f1a"\u3002<\/p>\n

        \u7a81\u7834\u65b9\u5411\u4e00：\u5177\u8eab\u7269\u7406\u73af\u5883\u4f5c\u4e3a RSI \u7684"\u6297\u4fd8\u83b7\u9a8c\u8bc1\u951a\u70b9"<\/h4>\n

        \u6838\u5fc3\u8bba\u70b9（\u63a8\u6f14\u4e2d\u6700\u6709\u4ef7\u503c\u7684\u539f\u521b\u6d1e\u89c1）：\u7269\u7406\u73af\u5883\u7684\u4ef7\u503c\u4e0d\u662f"\u4e0d\u53ef\u6b3a\u9a97"，\u800c\u662f"\u6b3a\u9a97\u6210\u672c\u4e0d\u5bf9\u79f0"\u2014\u2014\u64cd\u7eb5\u6587\u672c\u8bc4\u4f30\u5668\u53ea\u9700\u6539\u6743\u91cd\u5206\u5e03，\u64cd\u7eb5\u7269\u7406\u73af\u5883\u8981\u4ed8\u51fa\u771f\u5b9e\u80fd\u91cf\u4e0e\u65f6\u95f4\u4ee3\u4ef7\u3002\u66f4\u6df1\u4e00\u5c42：\u8de8\u901a\u9053\u6821\u9a8c\u7684\u6297\u4fd8\u83b7\u6027\u6765\u81ea\u7269\u7406\u5b9a\u5f8b\u7684\u7ed3\u6784\u521a\u6027（\u5404\u901a\u9053\u88ab\u5fae\u5206\u65b9\u7a0b\u8026\u5408，\u7be1\u6539\u4e00\u4e2a\u4f1a\u5728\u53e6\u4e00\u4e2a\u66b4\u9732）\u3002<\/p>\n

        \u914d\u5957\u7684\u5ea6\u91cf\u9636\u68af（\u6bcf\u4e00\u6b65\u90fd\u662f\u5bf9\u4e0a\u4e00\u6b65\u7f3a\u9677\u7684\u4fee\u590d）：<\/p>\n

        \u4e92\u4fe1\u606f\u8026\u5408\u5ea6
        \n \u2192 \u6270\u52a8\u54cd\u5e94\u5f39\u6027（\u54cd\u5e94\u5f62\u72b6\u6f02\u79fb = \u5df2\u88ab\u9002\u5e94）
        \n \u2192 \u8f6c\u79fb\u71b5\u65b9\u5411\u6027（\u7269\u7406\u2192\u597d\u5947 vs \u597d\u5947\u2192\u7269\u7406，\u53cd\u8f6c = \u5171\u8c0b）
        \n \u2192 \u5e72\u9884\u4e0d\u53d8\u6027（\u7ed3\u6784\u5316\u6270\u52a8\u524d\u540e\u56e0\u679c\u56fe\u5dee\u5f02，\u7ed5\u5f00\u65f6\u5e8f\u5047\u8bbe）
        \n \u2192 \u7269\u7406\u65f6\u95f4\u53cd\u6f14\u4e0d\u5bf9\u79f0\u6027（\u6700\u786c\u951a\u70b9：\u4f2a\u9020\u5b83\u9700\u8981\u6539\u5f15\u64ce\u6f14\u5316\u89c4\u5219）<\/p>\n

        \u6700\u5c0f\u53ef\u884c\u5b9e\u9a8c：\u76ee\u6807\u5e8f\u5217\u6301\u7eed\u53d8\u5316\u7684\u64cd\u4f5c\u4efb\u52a1，\u5bf9\u7167"\u7eaf\u81ea\u8bc4\u4f30 vs \u7269\u7406\u8fc7\u7a0b\u9884\u6d4b\u4e00\u81f4\u6027\u9a8c\u8bc1"，\u5224\u636e = \u81ea\u4f30\u6307\u6807\u4e0e\u771f\u5b9e\u6210\u529f\u7387\u7684\u76f8\u5173\u6027\u8870\u51cf\u66f2\u7ebf\u3002<\/p>\n

        \u7a81\u7834\u65b9\u5411\u4e8c：\u8fde\u63a5\u7ec4\u5148\u9a8c \u00d7 \u53cc\u901f\u7387\u51b2\u7a81\u4f18\u5316<\/h4>\n

        \u628a\u679c\u8747"\u51b2\u7a81\u5206\u79bb（\u6beb\u79d2\u7ea7）+ \u517c\u5bb9\u6574\u5408（\u5206\u949f-\u5c0f\u65f6\u7ea7）"\u5f62\u5f0f\u5316\u4e3a\u53cc\u901f\u7387\u4f18\u5316：\u5feb\u5faa\u73af\u5904\u7406\u5373\u65f6\u68af\u5ea6\u51b2\u7a81，\u6162\u5faa\u73af\u8c03\u6574\u6a21\u5757\u95f4\u6743\u91cd\u4fc3\u6210\u957f\u671f\u517c\u5bb9\u3002\u8fde\u63a5\u7ec4\u5148\u9a8c\u4e0d\u4f5c\u4e3a\u56fa\u5b9a\u7ea6\u675f，\u800c\u4f5c\u4e3a\u5e26\u8870\u51cf\u60e9\u7f5a\u7684\u521d\u59cb\u5316（\u201c\u4ece\u5148\u9a8c\u4e2d\u91ca\u653e\u201d）\u2014\u2014\u7ea6\u675f\u62d3\u6251\u7edf\u8ba1\u91cf（\u5206\u533a\u6a21\u5757\u5ea6\u3001\u957f\u7a0b\u7a00\u758f\u6027），\u4e0d\u7ea6\u675f\u5177\u4f53\u8fde\u63a5\u3002<\/p>\n

        \u7a81\u7834\u65b9\u5411\u4e09：\u4e09\u65f6\u95f4\u5c3a\u5ea6\u5206\u79bb + \u5173\u952e\u671f\u95e8\u63a7<\/h4>\n

        \u5feb = \u68af\u5ea6\u66f4\u65b0（\u7a81\u89e6），\u4e2d = \u5b9a\u671f\u7ed3\u6784\u526a\u679d\/\u751f\u957f（\u81ea\u751f\u957f），\u6162 = \u4ee3\u9645\u67b6\u6784\u9009\u62e9（\u81ea\u8fdb\u5316）\u3002\u5224\u636e\u8bbe\u8ba1\u5f88\u5173\u952e：\u4e09\u5c3a\u5ea6\u5168\u5f00\u8981\u663e\u8457\u4f18\u4e8e\u4efb\u4f55\u5b50\u96c6，\u4e14\u4f18\u52bf\u968f\u4efb\u52a1\u5e8f\u5217\u53d8\u957f\u800c\u6269\u5927\u3002\u7ed3\u6784\u6539\u53d8\u7684"\u5f00\u5173"\u7531\u72ec\u7acb\u8c03\u8d28\u901a\u9053\u63a7\u5236（\u5173\u952e\u671f\u7ba1\u7406），\u8c03\u8282\u65f6\u673a\u7684\u968f\u673a\u6027\u5e94\u6765\u81ea\u7cfb\u7edf\u5916\u4e0d\u53ef\u6a21\u62df\u6e90\u3002<\/p>\n

        \n

        6. \u7ed3\u8bed：\u679c\u8747\u4e0d\u662f\u7b54\u6848，\u662f\u5730\u56fe<\/h3>\n

        \u8c03\u7814\u6700\u540e\u56de\u5934\u770b，\u6700\u6709\u610f\u601d\u7684\u7ed3\u6784\u662f：AI \u5708\u548c\u795e\u7ecf\u79d1\u5b66\u5708\u5728 2026 \u5e74\u4ece\u4e24\u4e2a\u65b9\u5411\u722c\u5411\u4e86\u540c\u4e00\u5ea7\u5c71\u2014\u2014<\/p>\n

          \n
        • AI \u5708\u4ece RSI\/RL \u4fa7\u722c\u4e0a\u6765，\u53d1\u73b0\u74f6\u9888\u662f\u9a8c\u8bc1\u4fe1\u53f7\u4e0e\u591a\u65f6\u95f4\u5c3a\u5ea6\u8bb0\u5fc6\u7ec4\u7ec7；<\/li>\n
        • \u795e\u7ecf\u79d1\u5b66\u5708\u4ece\u679c\u8747\/\u4eba\u8111\u4fa7\u722c\u4e0a\u6765，\u521a\u597d\u628a"\u4ef7\u503c\u7cfb\u7edf + \u7a00\u758f\u7f16\u7801 + \u5206\u5c42\u8c03\u5236 + \u591a\u65f6\u95f4\u5c3a\u5ea6\u53ef\u5851\u6027"\u7684\u8bc1\u636e\u94fe\u5907\u9f50\u4e86；<\/li>\n
        • \u4e24\u8fb9\u5728"\u4e16\u754c\u6a21\u578b = \u5185\u90e8\u8bc4\u4f30\u5668"\u8fd9\u4e2a\u4f4d\u7f6e\u76f8\u9047\u3002<\/li>\n<\/ul>\n

          \u679c\u8747\u8111\u53ea\u6709 14 \u4e07\u795e\u7ecf\u5143（\u4eba\u8111\u7684 60 \u4e07\u5206\u4e4b\u4e00），\u4f46\u8611\u83c7\u4f53\u6d53\u7f29\u4e86\u6d77\u9a6c（\u8054\u60f3\u8bb0\u5fc6）\u4e0e\u5c0f\u8111（\u7a00\u758f\u6269\u5c55+\u5e76\u884c\u53ef\u5851\u6027）\u4e24\u4e2a\u7cfb\u7edf\u7684\u8ba1\u7b97\u57fa\u5e8f，\u800c\u5b83\u7684\u5b8c\u6574\u63a5\u7ebf\u56fe\u5df2\u7ecf\u8eba\u5728\u90a3\u513f\u4e86\u3002\u5c0f\u8111\u7cfb\u7edf\u7684\u539f\u7406 + \u5927\u8111\u7cfb\u7edf\u7684\u4efb\u52a1 + \u5177\u8eab\u73af\u5883\u7684\u9a8c\u8bc1\u2014\u2014\u8fd9\u4e2a\u7ec4\u5408\u76ee\u524d\u6ca1\u4eba\u505a\u8fc7\u3002<\/p>\n

          \u6216\u8bb8\u8fd9\u5c31\u662f\u8c03\u7814\u6700\u5927\u7684\u6536\u83b7：\u7a81\u7834\u70b9\u5f88\u5c11\u662f\u5168\u65b0\u7684，\u66f4\u591a\u662f\u4e24\u5f20\u5df2\u7ecf\u753b\u597d\u7684\u5730\u56fe\u4e0a，\u6070\u597d\u6ca1\u4eba\u8d70\u8fc7\u7684\u4ea4\u53c9\u8def\u53e3\u3002<\/p>\n

          \n

          \u9644：\u8bba\u6587\u4e0e\u8d44\u6e90\u7d22\u5f15（\u8282\u9009）<\/h3>\n

          RSI \u65b9\u5411<\/p>\n

            \n
          • The Last AI Built by Humans: Toward Genuine RSI \u2014 arXiv:2609.11873<\/li>\n
          • Recursive Self-Improvement of AI Research Agents \u2014 arXiv:2609.26457<\/li>\n
          • Darwin G\u00f6del Machine \u2014 arXiv:2505.22954 ｜ Red Queen G\u00f6del Machine \u2014 arXiv:2606.26294<\/li>\n
          • HELIX: Model-Harness Co-evolution \u2014 arXiv:2608.13951<\/li>\n
          • Phantom Gains: Auditing Self-Improvement Against a Measured Null \u2014 arXiv:2608.20290<\/li>\n
          • Awesome-RSI-Research（603 \u7bc7）\u2014 github.com\/Lee1003-lee\/Awesome-RSI-Research<\/li>\n<\/ul>\n

            \u4eff\u751f\u5927\u8111\u65b9\u5411<\/p>\n

              \n
            • FlyGCL: Brain-Inspired Hierarchical Modularity for GCL \u2014 arXiv:2609.25146（\u4ee3\u7801：THU-NeuroML\/FlyGCL）<\/li>\n
            • Connectome-constrained networks predict neural activity \u2014 Nature 2024（Lappalainen et al.）<\/li>\n
            • Sniffing Shapes Dopamine Signals \u2014 bioRxiv 2026-04<\/li>\n
            • \u679c\u8747\u96c4\u6027 CNS \u5b8c\u6574\u8fde\u63a5\u7ec4 \u2014 HHMI\/Janelia，2026-09-03 \u53d1\u5e03<\/li>\n
            • Bee-Nav（Nature 2026）｜ Antcar（Nat. Commun. 2025）<\/li>\n
            • Goal-directed learning in cortical organoids \u2014 Cell 2026<\/li>\n<\/ul>\n

              RL \/ \u4e16\u754c\u6a21\u578b\u65b9\u5411<\/p>\n

                \n
              • RLVR Implicitly Extends Reasoning Beyond Sampling Efficiency \u2014 ICLR 2026<\/li>\n
              • DAPO ｜ Entropy-Preserving RL ｜ NashDreamer \u2014 arXiv 2026-09<\/li>\n
              • Scaling Automatic Research Agents via World Models \u2014 arXiv:2608.12564<\/li>\n
              • V-JEPA 2-AC（Meta, 2025）｜ Physical State Grounding for JEPA World Models（2026-08）<\/li>\n<\/ul>\n

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