{"id":78364,"date":"2026-02-27T09:49:54","date_gmt":"2026-02-27T01:49:54","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/78364.html"},"modified":"2026-02-27T09:49:54","modified_gmt":"2026-02-27T01:49:54","slug":"ttrv%ef%bc%9a%e9%9d%a2%e5%90%91%e8%a7%86%e8%a7%89-%e8%af%ad%e8%a8%80%e6%a8%a1%e5%9e%8b%e7%9a%84%e6%b5%8b%e8%af%95-%e6%97%b6%e5%bc%ba%e5%8c%96%e5%ad%a6%e4%b9%a0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/78364.html","title":{"rendered":"TTRV\uff1a\u9762\u5411\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b\u7684\u6d4b\u8bd5-\u65f6\u5f3a\u5316\u5b66\u4e60"},"content":{"rendered":"

25\u5e7412\u6708\u6765\u81ea\u5370\u5ea6IIS\u3001JHU\u3001\u65af\u5766\u798f\u3001\u5fb7\u56fdTuebingen AI\u4e2d\u5fc3\u3001MIT-IBM\u8054\u5408\u5b9e\u9a8c\u5ba4\u548cMIT- CSAIL\u7684\u8bba\u6587\u201cTTRV: Test-Time Reinforcement Learning for Vision Language Models\u201d\u3002<\/p>\n

\u73b0\u6709\u7684\u5f3a\u5316\u5b66\u4e60\u5956\u52b1\u4fe1\u53f7\u63d0\u53d6\u65b9\u6cd5\u901a\u5e38\u4f9d\u8d56\u4e8e\u6807\u6ce8\u6570\u636e\u548c\u4e13\u95e8\u7684\u8bad\u7ec3\u96c6\u5212\u5206，\u8fd9\u4e0e\u4eba\u7c7b\u76f4\u63a5\u4ece\u73af\u5883\u4e2d\u5b66\u4e60\u7684\u65b9\u5f0f\u622a\u7136\u4e0d\u540c\u3002\u672c\u6587\u63d0\u51fa\u4e00\u79cd\u540d\u4e3aTTRV\u7684\u7b97\u6cd5，\u901a\u8fc7\u5728\u63a8\u7406\u65f6\u52a8\u6001\u8c03\u6574\u6a21\u578b\u6765\u589e\u5f3a\u89c6\u89c9\u8bed\u8a00\u7406\u89e3\u80fd\u529b，\u800c\u65e0\u9700\u4efb\u4f55\u6807\u6ce8\u6570\u636e\u3002\u5177\u4f53\u800c\u8a00，\u6539\u8fdb\u7ec4\u76f8\u5bf9\u7b56\u7565\u4f18\u5316（GRPO）\u6846\u67b6，\u5728\u5bf9\u6bcf\u4e2a\u6d4b\u8bd5\u6837\u672c\u8fdb\u884c\u591a\u6b21\u63a8\u7406\u7684\u540c\u65f6，\u6839\u636e\u57fa\u7840\u6a21\u578b\u8f93\u51fa\u7684\u9891\u7387\u8bbe\u8ba1\u5956\u52b1\u3002\u6b64\u5916，\u8fd8\u63d0\u51fa\u901a\u8fc7\u540c\u65f6\u5956\u52b1\u6a21\u200b\u200b\u578b\u83b7\u5f97\u4f4e\u71b5\u8f93\u51fa\u7ecf\u9a8c\u5206\u5e03\u6765\u63a7\u5236\u6a21\u578b\u8f93\u51fa\u7684\u591a\u6837\u6027\u3002\u8be5\u65b9\u6cd5\u5728\u7269\u4f53\u8bc6\u522b\u548c\u89c6\u89c9\u95ee\u7b54（VQA）\u4efb\u52a1\u4e0a\u5747\u53d6\u5f97\u6301\u7eed\u7684\u63d0\u5347，\u5206\u522b\u63d0\u5347\u9ad8\u8fbe 52.4% \u548c 29.8%，\u5728 16 \u4e2a\u6570\u636e\u96c6\u4e0a\u7684\u5e73\u5747\u63d0\u5347\u5206\u522b\u4e3a 24.6% \u548c 10.0%\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f，\u5728\u56fe\u50cf\u8bc6\u522b\u4efb\u52a1\u4e2d，\u5e94\u7528\u4e8e Intern-VL-8B \u7684 TTRV \u5728 8 \u4e2a\u57fa\u51c6\u6d4b\u8bd5\u4e2d\u5e73\u5747\u8d85\u8d8a GPT-4o 2.3%，\u540c\u65f6\u5728 VQA \u4efb\u52a1\u4e0a\u4e5f\u4fdd\u6301\u5f88\u5f3a\u7684\u7ade\u4e89\u529b，\u8fd9\u8868\u660e\u6d4b\u8bd5-\u65f6\u5f3a\u5316\u5b66\u4e60\u53ef\u4ee5\u8fbe\u5230\u751a\u81f3\u8d85\u8d8a\u6700\u5f3a\u5927\u7684\u4e13\u6709\u6a21\u578b\u3002\u6700\u540e，\u6d4b\u8bd5-\u65f6\u5f3a\u5316\u5b66\u4e60\u5728\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b（VLM）\u65b9\u9762\u7684\u8bb8\u591a\u6709\u8da3\u7279\u6027：\u4f8b\u5982，\u5373\u4f7f\u5728\u6570\u636e\u6781\u5176\u53d7\u9650\u7684\u60c5\u51b5\u4e0b，\u4ec5\u5bf9\u4e00\u4e2a\u968f\u673a\u9009\u62e9\u7684\u672a\u6807\u8bb0\u6d4b\u8bd5\u6837\u672c\u8fdb\u884c\u81ea\u9002\u5e94\u8bad\u7ec3，TTRV \u5728\u8bc6\u522b\u4efb\u52a1\u4e2d\u4ecd\u7136\u80fd\u591f\u5e26\u6765\u9ad8\u8fbe 5.5% \u7684\u663e\u8457\u63d0\u5347\u3002<\/p>\n

\n

\u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u7684\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b（VLM）\u5fae\u8c03\u3002\u5f3a\u5316\u5b66\u4e60\u5df2\u6210\u4e3a\u4f7f\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u4e0e\u4eba\u7c7b\u504f\u597d\u548c\u4efb\u52a1\u76ee\u6807\u4fdd\u6301\u4e00\u81f4\u7684\u6838\u5fc3\u8303\u5f0f，\u8bf8\u5982RLHF[43]\u548cDPO[47]\u7b49\u65b9\u6cd5\u63d0\u9ad8\u4e86\u8bed\u8a00\u6a21\u578b\u548c\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b\u7684\u5b89\u5168\u6027\u3001\u8fde\u8d2f\u6027\u548c\u6307\u4ee4\u9075\u5faa\u80fd\u529b\u3002\u6700\u8fd1，\u8bf8\u5982GRPO[53]\u7b49\u57fa\u4e8e\u89c4\u5219\u7684\u65b9\u6cd5\u8bc1\u660e\u6269\u5c55\u5f3a\u5316\u5b66\u4e60\u4ee5\u589e\u5f3a\u63a8\u7406\u80fd\u529b\u7684\u53ef\u884c\u6027\u3002\u5728\u6b64\u57fa\u7840\u4e0a，\u57fa\u4e8e\u5f3a\u5316\u5b66\u4e60\u7684\u5fae\u8c03（RFT）\u5df2\u88ab\u6269\u5c55\u5230\u5404\u79cd\u89c6\u89c9\u9a71\u52a8\u4efb\u52a1\u7684\u591a\u6a21\u6001\u6a21\u578b\u3002\u4f8b\u5982，VLM-R1[54]\u3001VisualThinker-R1-Zero[75]\u548cPerception-R1[69]\u5206\u522b\u5c06RFT\u5e94\u7528\u4e8e\u5f00\u653e\u8bcd\u6c47\u7269\u4f53\u8bc6\u522b\u3001\u7a7a\u95f4\u63a8\u7406\u548c\u89c6\u89c9\u611f\u77e5；CLS-RL[26]\u5c06RFT\u5e94\u7528\u4e8e\u5c11\u6837\u672c\u56fe\u50cf\u5206\u7c7b； R1-VL [71] \u53ca\u76f8\u5173\u7814\u7a76\u8fdb\u4e00\u6b65\u5b8c\u5584\u591a\u6a21\u6001\u63a8\u7406\u3002\u8fd9\u4e9b\u5de5\u4f5c\u8868\u660e，\u5f3a\u5316\u5b66\u4e60\u53ef\u4ee5\u663e\u8457\u589e\u5f3a\u89c6\u89c9\u8bed\u8a00\u6a21\u578b（VLM）\u7684\u89c6\u89c9\u4e2d\u5fc3\u80fd\u529b，\u4f46\u5b83\u4eec\u4ecd\u7136\u4f9d\u8d56\u4e8e\u7cbe\u5fc3\u8bbe\u8ba1\u7684\u8bad\u7ec3\u96c6\u6216\u5e26\u6807\u7b7e\u7684\u53cd\u9988\u3002<\/p>\n

\u6d4b\u8bd5-\u65f6\u8bad\u7ec3（TTT）\u3002TTT \u65b9\u6cd5\u5728\u63a8\u7406\u65f6\u8c03\u6574\u6a21\u578b\u53c2\u6570，\u800c\u65e0\u9700\u5e26\u6807\u7b7e\u7684\u6d4b\u8bd5\u6570\u636e，\u901a\u5e38\u662f\u901a\u8fc7\u4f18\u5316\u66ff\u4ee3\u76ee\u6807，\u4f8b\u5982\u6700\u5c0f\u5316\u71b5\u6216\u81ea\u76d1\u7763\u8f85\u52a9\u635f\u5931 [16, 35, 38, 49, 57, 58, 60]\u3002\u8fd9\u4e9b\u6280\u672f\u6700\u521d\u662f\u4e3a\u5355\u6a21\u6001\u67b6\u6784\u5f00\u53d1\u7684，\u4f46\u6700\u8fd1\u5df2\u88ab\u6269\u5c55\u5230\u591a\u6a21\u6001\u7cfb\u7edf，\u5176\u4e2d\u5927\u90e8\u5206\u7814\u7a76\u96c6\u4e2d\u5728\u53cc\u7f16\u7801\u5668 VLM \u4e0a（\u4f8b\u5982 CLIP [46]）\u3002\u5178\u578b\u7684\u4f8b\u5b50\u5305\u62ec TPT [55]，\u5b83\u901a\u8fc7\u6700\u5c0f\u5316\u589e\u5f3a\u89c6\u56fe\u7684\u71b5\u6765\u8c03\u6574\u6587\u672c\u63d0\u793a，\u4ee5\u53ca\u5176\u6269\u5c55 DiffTPT [15] \u548c C-TPT [68]，\u5b83\u4eec\u5206\u522b\u63d0\u9ad8\u4e86\u589e\u5f3a\u8d28\u91cf\u548c\u6821\u51c6\u7cbe\u5ea6\u3002RLCF [74] \u5219\u5229\u7528\u6765\u81ea\u66f4\u5927\u6a21\u578b\u7684\u53cd\u9988\u6765\u8c03\u6574\u56fe\u50cf\u7f16\u7801\u5668，\u800c\u4e00\u4e9b\u9ed1\u76d2\u5b50\u65b9\u6cd5\u5219\u76f4\u63a5\u64cd\u4f5c\u5d4c\u5165，\u65e0\u9700\u4fee\u6539\u5185\u90e8\u53c2\u6570\u3002<\/p>\n

\u7136\u800c，\u8bb8\u591a\u5e7f\u6cdb\u4f7f\u7528\u7684 TTT \u65b9\u6cd5\u90fd\u65bd\u52a0\u67b6\u6784\u6216\u76ee\u6807\u5c42\u9762\u7684\u7ea6\u675f，\u9650\u5236\u5b83\u4eec\u5728\u57fa\u4e8e\u89e3\u7801\u5668\u7684 VLM \u4e2d\u7684\u5e94\u7528\u3002\u4f8b\u5982，Norm [49] \u548c DUA [35] \u9700\u8981\u6279\u5f52\u4e00\u5316\u5c42，\u800c TENT [60] \u5219\u4f9d\u8d56\u4e8e\u6700\u5c0f\u5316\u6a21\u578b\u8f93\u51fa\u7c7b\u522b\u6982\u7387\u5206\u5e03\u7684\u71b5\u3002\u57fa\u4e8e\u89e3\u7801\u5668\u7684 VLM \u4e0d\u4f1a\u751f\u6210\u6b64\u7c7b\u7c7b\u522b\u7ea7\u5206\u5e03；\u76f8\u53cd，\u5b83\u4eec\u4f1a\u751f\u6210\u57fa\u4e8e\u5b8c\u6574\u8bcd\u6c47\u8868\u7684\u81ea\u56de\u5f52token\u5206\u5e03，\u8fd9\u4f7f\u5f97\u76f4\u63a5\u5e94\u7528 TENT \u5f0f\u76ee\u6807\u5e76\u975e\u6613\u4e8b\u3002<\/p>\n

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\n\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/h3>\n

TTRV\u7684\u76ee\u6807\u662f\u901a\u8fc7\u76f4\u63a5\u4ece\u9047\u5230\u7684\u672a\u6807\u8bb0\u6d4b\u8bd5\u6570\u636e\u4e2d\u63d0\u53d6\u5956\u52b1\u4fe1\u53f7\u6765\u6539\u8fdb\u4e0b\u6e38\u89c6\u89c9\u4efb\u52a1\u3002\u4e3a\u6b64，\u7528\u7ec4\u76f8\u5bf9\u7b56\u7565\u4f18\u5316（GRPO）[53]\u5bf9\u4e00\u4e2a\u73b0\u6210\u7684\u89c6\u89c9\u8bed\u8a00\u6a21\u578b（\u4f8b\u5982InternVL [77]）\u8fdb\u884c\u5f15\u5bfc\u3002\u5de5\u4f5c\u7684\u4e00\u4e2a\u5173\u952e\u8d21\u732e\u5728\u4e8e\u8bbe\u8ba1\u5b8c\u5168\u65e0\u76d1\u7763\u7684\u5956\u52b1\u4fe1\u53f7\u3002\u6982\u62ec\u6765\u8bf4，\u5f15\u5165\u4e24\u79cd\u4e92\u8865\u7684\u5956\u52b1：（i）\u57fa\u4e8e\u9891\u7387\u7684\u5956\u52b1，\u7528\u4e8e\u9f13\u52b1\u57fa\u7840\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\u7ed9\u51fa\u4e00\u81f4\u7684\u7b54\u6848；（ii）\u57fa\u4e8e\u71b5\u7684\u5956\u52b1，\u7528\u4e8e\u89c4\u8303\u54cd\u5e94\u7684\u591a\u6837\u6027\u3002\u5982\u56fe\u6982\u8ff0\u65b9\u6cd5：
\n\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u57fa\u4e8e\u5206\u5e03\u5956\u52b1\u7684\u6d4b\u8bd5-\u65f6\u5f3a\u5316\u5b66\u4e60<\/h3>\n

TTRV \u6269\u5c55\u901a\u5e38\u901a\u8fc7\u6807\u8bb0\u6570\u636e\u63d0\u53d6\u5956\u52b1\u7684\u4f20\u7edf GRPO \u6846\u67b6，\u5f15\u5165\u63a8\u7406-\u65f6\u5f3a\u5316\u5b66\u4e60\u6846\u67b6\u3002\u63d0\u51fa\u5728\u63a8\u7406\u65f6\u76f4\u63a5\u4ece\u6a21\u578b\u8f93\u51fa\u7684\u7ecf\u9a8c\u5206\u5e03\u4e2d\u63d0\u53d6\u81ea\u76d1\u7763\u5f3a\u5316\u4fe1\u53f7\u3002\u4e0e\u4f9d\u8d56\u5916\u90e8\u76d1\u7763\u7684\u8bbe\u7f6e\u4e0d\u540c，\u6846\u67b6\u751f\u6210\u81ea\u6d3d\u5956\u52b1，\u5229\u7528\u5c55\u5f00\u7684\u53d8\u5f02\u6027\u6765\u6307\u5bfc\u63a8\u7406\u8fc7\u7a0b\u4e2d\u7684\u6536\u655b\u3002\u5177\u4f53\u800c\u8a00，\u4ece\u672a\u6807\u8bb0\u6570\u636e\u4e2d\u63d0\u53d6\u4e24\u79cd\u5956\u52b1\u3002<\/p>\n

\u57fa\u4e8e\u9891\u7387\u7684\u5956\u52b1\u3002\u7ed9\u5b9a\u6d4b\u8bd5\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u7279\u5b9a\u65f6\u95f4\u6b65\u7684\u6a21\u578b\u526f\u672c，\u76ee\u6807\u662f\u591a\u6b21\u5bf9\u6d4b\u8bd5\u6837\u672c\u8fdb\u884c\u63a8\u7406，\u5e76\u6839\u636e\u9884\u6d4b\u51fa\u73b0\u7684\u9891\u7387\u7ed9\u4e88\u5956\u52b1\u3002\u5176\u57fa\u672c\u539f\u7406\u662f，\u6a21\u578b\u4ea7\u751f\u7684\u54cd\u5e94\u8d8a\u4e00\u81f4，\u5176\u6b63\u786e\u7684\u53ef\u80fd\u6027\u5c31\u8d8a\u5927\u3002\u5f62\u5f0f\u4e0a，\u5bf9\u4e8e\u6bcf\u4e2a\u6d4b\u8bd5\u6837\u672c x（\u7531\u56fe\u50cf\u548c\u6587\u672c\u63d0\u793a\u7ec4\u6210），\u4ece\u5f53\u524d\u7b56\u7565 \u03c0_\u03b8(\u00b7 | x) \u4e2d\u62bd\u53d6 N \u4e2a\u5019\u9009\u54cd\u5e94 {y\u02c6_1 , y\u02c6_2 , . . . , y\u02c6_N }\u3002\u4ee4 U = {y\u0303_1, y\u0303_2, \u2026, y\u0303_M} \u8868\u793a\u552f\u4e00\u8f93\u51fa\u7684\u96c6\u5408\u3002\u4f30\u8ba1 y\u0303_m \u7684\u7ecf\u9a8c\u6982\u7387 p(y \u0303_m)，\u7136\u540e\u5b9a\u4e49\u5355\u4e2a\u6837\u672c y\u02c6_j \u7684\u5956\u52b1 r_1(y\u02c6_j)，\u8be5\u5956\u52b1\u5bf9\u9891\u7e41\u51fa\u73b0\u7684\u54cd\u5e94\u8d4b\u4e88\u66f4\u9ad8\u7684\u503c，\u540c\u65f6\u4ecd\u7136\u5bf9\u4e0d\u5e38\u89c1\u4f46\u53ef\u80fd\u6709\u610f\u4e49\u7684\u5907\u9009\u65b9\u6848\u5206\u914d\u975e\u96f6\u5956\u52b1\u3002\u8fd9\u79cd\u5206\u7ea7\u7ed3\u6784\u6355\u6349\u91cd\u590d\u63a8\u5e7f\u8fc7\u7a0b\u4e2d\u9690\u542b\u7684\u5171\u8bc6，\u800c\u4e0d\u4f1a\u4e22\u5f03\u5c11\u6570\u6d3e\u63a8\u7406\u8def\u5f84\u3002<\/p>\n

\u91cd\u8981\u7684\u662f，\u8fd9\u4e0e [78] \u4e2d\u4f7f\u7528\u7684\u6807\u51c6 N \u9009\u6700\u4f73\u91c7\u6837\u65b9\u6848\u4e0d\u540c，\u540e\u8005\u4ec5\u9009\u62e9\u6700\u9891\u7e41\u51fa\u73b0\u7684\u54cd\u5e94\u5e76\u4e22\u5f03\u6240\u6709\u5176\u4ed6\u54cd\u5e94\u3002\u5f53\u6a21\u578b\u4e0d\u786e\u5b9a\u6216\u6700\u9891\u7e41\u7684\u9884\u6d4b\u4e0d\u6b63\u786e\u65f6，\u8fd9\u79cd\u786c\u6027\u51b3\u7b56\u53ef\u80fd\u4f1a\u51fa\u73b0\u95ee\u9898，\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e00\u4e2a\u8bef\u5bfc\u6027\u5f3a\u4f46\u53ef\u80fd\u9519\u8bef\u7684\u5956\u52b1\u4fe1\u53f7\u3002\u76f8\u6bd4\u4e4b\u4e0b，\u5956\u52b1\u516c\u5f0f\u4ea7\u751f\u4e00\u4e2a\u67d4\u548c\u7684\u3001\u6982\u7387\u6027\u7684\u76d1\u7763\u4fe1\u53f7，\u53cd\u6620\u54cd\u5e94\u7684\u5b8c\u6574\u5206\u5e03\u3002\u8fd9\u79cd\u89c6\u89d2\u81ea\u7136\u4e0e\u8d1d\u53f6\u65af\u63a8\u7406\u76f8\u8054\u7cfb：\u672c\u65b9\u6cd5\u4e0d\u662f\u7b80\u5316\u4e3a\u5355\u4e00\u7684\u70b9\u4f30\u8ba1，\u800c\u662f\u4fdd\u7559\u5047\u8bbe\u7684\u4e0d\u786e\u5b9a\u6027\u5e76\u5229\u7528\u5b83\u6765\u6307\u5bfc\u5b66\u4e60\u3002<\/p>\n

\u591a\u6837\u6027\u63a7\u5236\u5956\u52b1\u3002\u4e3a\u4e86\u8865\u5145\u57fa\u4e8e\u9891\u7387\u7684\u5956\u52b1 r_1（\u5b83\u4e3a\u91cd\u590d\u7684\u6a21\u578b\u54cd\u5e94\u63d0\u4f9b\u4e0e\u9891\u7387\u6210\u6bd4\u4f8b\u7684\u8f6f\u6027\u5956\u52b1），\u5f15\u5165\u4e00\u4e2a\u57fa\u4e8e\u71b5\u7684\u6b63\u5219\u5316\u9879\u6765\u63a7\u5236\u6536\u655b\u6027\u3002\u5bf9\u4e8e\u7ed9\u5b9a\u7684\u6d4b\u8bd5\u6837\u672c，\u8ba1\u7b97\u7ecf\u9a8c\u54cd\u5e94\u5206\u5e03\u7684\u9999\u519c\u71b5 [51] H（P），\u5e76\u5b9a\u4e49\u8f85\u52a9\u5956\u52b1 r_2 = \u2212H\u00a7，\u7528\u4e8e\u60e9\u7f5a\u8f93\u51fa\u5206\u5e03\u4e2d\u8fc7\u5ea6\u5206\u6563\u7684\u60c5\u51b5\u3002\u8be5\u673a\u5236\u786e\u4fdd\u6a21\u578b\u5728\u521d\u59cb\u9636\u6bb5\u63a2\u7d22\u591a\u79cd\u63a8\u7406\u6a21\u5f0f（\u5982\u57fa\u4e8e\u9891\u7387\u7684\u5956\u52b1\u6240\u9f13\u52b1\u7684\u90a3\u6837），\u5e76\u9010\u6e10\u5c06\u6982\u7387\u96c6\u4e2d\u4e8e\u7a33\u5b9a\u7684\u9ad8\u6982\u7387\u7b54\u6848，\u800c\u4e0d\u662f\u5c06\u6ce8\u610f\u529b\u5206\u6563\u5230\u5197\u4f59\u7684\u54cd\u5e94\u4e0a\u3002<\/p>\n

\u7ec4\u5408\u5956\u52b1\u3002\u5206\u914d\u7ed9\u54cd\u5e94 y\u02c6_j \u7684\u603b\u4f53\u5956\u52b1\u662f\u6982\u7387\u9879\u548c\u71b5\u9879\u7684\u7ec4\u5408：R(y\u02c6_j) = r_1(y\u02c6_j) + \u03b1r_2，\u5176\u4e2d \u03b1 \u662f\u4e00\u4e2a\u53ef\u8c03\u8d85\u53c2\u6570，\u7528\u4e8e\u63a7\u5236\u6536\u655b\u6027\u548c\u591a\u6837\u6027\u4e4b\u95f4\u7684\u6743\u8861\u3002\u901a\u8fc7\u5c06\u57fa\u4e8e\u6982\u7387\u7684\u81ea\u6211\u5956\u52b1\u4e0e\u71b5\u6b63\u5219\u5316\u76f8\u7ed3\u5408，\u6a21\u578b\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u81ea\u9002\u5e94\u5730\u8c03\u6574\u5176\u8f93\u51fa，\u4ece\u800c\u5728\u63a2\u7d22\u591a\u6837\u5316\u7684\u63a8\u7406\u8def\u5f84\u548c\u6536\u655b\u5230\u4e00\u81f4\u7684\u9884\u6d4b\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002<\/p>\n

\u4f18\u5316\u76ee\u6807<\/h3>\n

\u5f3a\u5316\u5b66\u4e60\u7684\u76ee\u6807\u662f\u5728\u7b56\u7565\u4e0b\u6700\u5927\u5316\u9884\u671f\u5956\u52b1\u3002\u5bf9\u4e8e\u57fa\u4e8e\u89e3\u7801\u5668\u7684\u89c6\u89c9-\u8bed\u8a00\u6a21\u578b（VLM），\u4f18\u5316\u901a\u8fc7\u6807\u51c6\u7684\u81ea\u56de\u5f52\u8bed\u8a00\u5efa\u6a21\u76ee\u6807\u6765\u5b9e\u73b0，\u5956\u52b1\u63d0\u4f9b\u9884\u6d4btoken\u7684\u8f6f\u6837\u672c-\u7ea7\u52a0\u6743\u3002\u53c2\u6570\u901a\u8fc7\u68af\u5ea6\u4e0a\u5347\u6cd5\u66f4\u65b0\u3002<\/p>\n

GRPO [53] \u5bf9\u6b64\u8fc7\u7a0b\u8fdb\u884c\u6539\u8fdb，\u5c06\u539f\u59cb\u5956\u52b1\u66ff\u6362\u4e3a\u76f8\u5bf9\u4f18\u52bf\u3002\u8fd9\u4f7f\u5f97\u4f18\u5316\u4ece\u7edd\u5bf9\u5956\u52b1\u8f6c\u5411\u76f8\u5bf9\u6bd4\u8f83，\u4ece\u800c\u66f4\u52a0\u7a33\u5b9a，\u5e76\u66f4\u597d\u5730\u4e0e\u7ec4-\u7ea7\u76ee\u6807\u4fdd\u6301\u4e00\u81f4\u3002<\/p>\n

\u57fa\u7ebf：\u4e3a\u4e86\u8fdb\u884c\u6bd4\u8f83，\u8bc4\u4f30\u4ee5\u4e0b\u53cc\u7f16\u7801\u5668 VLM：CLIP [46]\u3001MetaCLIP [66]\u3001EVA-CLIP [56] \u548c SigLIP [70]\u3002\u4f5c\u4e3a\u57fa\u4e8e\u89e3\u7801\u5668\u7684 VLM \u7684\u4ee3\u8868\u6027\u65b9\u6cd5，\u9009\u62e9：LLaMA [59]\u3001LLaVA [30] \u548c Phi-3.5-vision [1]，\u5e76\u63d0\u4f9b\u4e13\u6709\u6a21\u578b GPT-4o [42] \u7684\u7ed3\u679c\u3002\u5728\u4e3b\u8981\u5b9e\u9a8c\u4e2d，\u5c06 TTRV \u5e94\u7528\u4e8e InternVL [6] \u7cfb\u5217\u7684\u4e0d\u540c\u6a21\u578b\u5c3a\u5bf8\u3002\u4f46\u662f，TTRV \u53ef\u4ee5\u5e94\u7528\u4e8e\u4efb\u4f55\u5f00\u6e90 VLM\u3002<\/p>\n

\u5b9e\u73b0\u7ec6\u8282：\u72ec\u7acb\u5730\u5c06 TTRV \u5e94\u7528\u4e8e\u6bcf\u4e2a\u57fa\u51c6\u6d4b\u8bd5\u3002\u4e3a\u4e86\u8fdb\u884c\u4f18\u5316，\u91c7\u7528 AdamW \u4f18\u5316\u5668，\u5e76\u4f7f\u7528\u4f59\u5f26\u5b66\u4e60\u7387\u8c03\u5ea6，\u5c06\u5cf0\u503c\u5b66\u4e60\u7387\u8bbe\u7f6e\u4e3a 5 \u00d7 10\u207b\u2077\u3002\u5728\u5c55\u5f00\u9636\u6bb5，\u751f\u6210 32 \u4e2a\u5019\u9009\u54cd\u5e94，\u6240\u6709\u5b9e\u9a8c\u7684\u6e29\u5ea6\u5747\u4e3a 1.0\u3002\u5956\u52b1\u8d85\u53c2\u6570 \u03b1 \u5728\u6240\u6709\u6570\u636e\u96c6\u4e0a\u5747\u56fa\u5b9a\u4e3a 0.75\u3002\u5c06\u6700\u5927\u63d0\u793a\u957f\u5ea6\u9650\u5236\u4e3a 7524 \u4e2a token，\u6700\u5927\u54cd\u5e94\u957f\u5ea6\u9650\u5236\u4e3a 1024 \u4e2a token\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

25\u5e7412\u6708\u6765\u81ea\u5370\u5ea6IIS\u3001JHU\u3001\u65af\u5766\u798f\u3001\u5fb7\u56fdTuebingen AI\u4e2d\u5fc3\u3001MIT-IBM\u8054\u5408\u5b9e\u9a8c\u5ba4\u548cMIT- CSAIL\u7684\u8bba\u6587\u201cTTRV: Test-Time Reinforcement Learning for Vision Language Models\u201d\u3002
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