{"id":8560,"date":"2026-05-29T02:41:02","date_gmt":"2026-05-28T18:41:02","guid":{"rendered":"https:\/\/infernews.com\/blog\/icml-2026-quot-prorl-effective-reinforcement-learning-for-proactive-recommendati\/"},"modified":"2026-05-29T02:41:02","modified_gmt":"2026-05-28T18:41:02","slug":"icml-2026-quot-prorl-effective-reinforcement-learning-for-proactive-recommendati","status":"publish","type":"post","link":"https:\/\/infernews.com\/blog\/icml-2026-quot-prorl-effective-reinforcement-learning-for-proactive-recommendati\/","title":{"rendered":"ProRL\uff1a\u628a\u63a8\u85a6\u8b8a\u6210\u6709\u8def\u7dda\u7684\u5f15\u5c0e"},"content":{"rendered":"<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/infernews.com\/blog\/wp-content\/uploads\/2026\/05\/framework-978812c9c2e2.jpg\" alt=\"ProRL Framework\"><\/figure>\n<p>ProRL \u4e0d\u662f\u4e00\u822c\u53ea\u4f30\u4f60\u4e0b\u4e00\u6b21\u6703\u6309\u751a\u9ebc\u7684\u63a8\u85a6\u6a21\u578b\uff0c\u800c\u662f\u5148\u5b89\u6392\u5e7e\u500b\u4e2d\u9593\u9805\u76ee\uff0c\u6162\u6162\u628a\u8208\u8da3\u5e36\u5411\u76ee\u6a19\u9805\u76ee\u3002\u5c0d\u5f71\u7247\u5e73\u53f0\u6216\u96fb\u5546\u4f86\u8aaa\uff0c\u9019\u6bd4\u76f4\u63a5\u786c\u63a8\u964c\u751f\u5167\u5bb9\u66f4\u6709\u7b56\u7565\u3002<\/p>\n<p>\u9805\u76ee\u628a semantic-ID \u8868\u793a\u548c\u5f37\u5316\u5b78\u7fd2\u7d50\u5408\uff0c\u5148\u7528\u77ed\u78bc\u63cf\u8ff0\u9805\u76ee\uff0c\u518d\u5b78\u4e00\u689d\u63a8\u85a6\u8def\u5f91\u3002\u8a55\u5206\u4e0d\u53ea\u770b\u9ede\u64ca\uff0c\u9084\u540c\u6642\u770b\u76ee\u6a19\u8208\u8da3\u63d0\u5347\u3001\u76ee\u6a19\u6392\u540d\u63d0\u5347\uff0c\u4ee5\u53ca\u4e2d\u9593\u9805\u76ee\u7684\u9ede\u64ca\u7387\u3002<\/p>\n<ul>\n<li><strong>\u591a\u76ee\u6a19\u8a2d\u8a08<\/strong>\uff1a\u628a IoI\u3001IoR\u3001CTR \u4e00\u8d77\u7d0d\u5165\uff0c\u65b9\u5411\u8f03\u5b8c\u6574\u3002<\/li>\n<li><strong>\u65b9\u6cd5\u4eae\u9ede<\/strong>\uff1a\u52a0\u5165 Stepwise Reward Centering \u8207 Position-Specific Advantage Estimation\uff0c\u91cd\u9ede\u662f\u4fee\u6b63\u9577\u8def\u5f91\u504f\u5dee\u4e26\u6e1b\u5c11\u8a13\u7df4\u566a\u97f3\u3002<\/li>\n<li><strong>\u8a13\u7df4\u7a69\u5b9a\u6027<\/strong>\uff1a\u914d\u5408\u9810\u8a13\u7df4\u53c3\u8003\u7b56\u7565\u548c KL \u6b63\u5247\uff0c\u4ee4\u8a13\u7df4\u66f4\u7a69\u5b9a\u3002<\/li>\n<li><strong>\u5de5\u7a0b\u5c64\u9762<\/strong>\uff1a\u652f\u63f4 Accelerate \u591a GPU\uff0c\u8f03\u9069\u5408\u7814\u7a76\u74b0\u5883\u3002<\/li>\n<\/ul>\n<p>\u9019\u500b\u5009\u5eab\u5df2\u5206\u6210\u9810\u8a13\u7df4\u548c\u5f37\u5316\u5b78\u7fd2\u5169\u6bb5\uff0c\u4ea6\u63d0\u4f9b\u53ef\u76f4\u63a5\u8dd1\u7684 scripts\uff0ccheckpoint \u8207\u65e5\u8a8c\u6703\u81ea\u52d5\u4fdd\u5b58\u3002\u4f7f\u7528\u6642\u6700\u597d\u5df2\u6709 Python 3.11\u3001CUDA 12.4 \u548c\u591a GPU \u689d\u4ef6\uff0c\u5426\u5247\u8f03\u96e3\u5b8c\u6574\u91cd\u73fe\u3002<\/p>\n<p>\u8ad6\u6587\u6458\u8981\u6307\u51fa\uff0c\u5b83\u5728\u4e09\u500b\u771f\u5be6\u6578\u64da\u96c6\u4e0a\u52dd\u904e\u73fe\u6709\u4e3b\u52d5\u63a8\u85a6\u65b9\u6cd5\u3002\u6574\u9ad4\u4f86\u770b\uff0c\u9019\u500b\u9805\u76ee\u8f03\u9069\u5408\u505a\u63a8\u85a6\u7814\u7a76\u3001\u5e8f\u5217\u6c7a\u7b56\u5be6\u9a57\uff0c\u6216\u60f3\u4e86\u89e3 ProRL\u3001\u9810\u8a13\u7df4\u53c3\u8003\u7b56\u7565\u8207 semantic-ID \u5982\u4f55\u914d\u5408\u7684\u4eba\u3002<\/p>\n<p><strong>GitHub\uff1a<\/strong> <a href=\"https:\/\/github.com\/hongruhou89\/ProRL\" rel=\"noopener noreferrer\">https:\/\/github.com\/hongruhou89\/ProRL<\/a><\/p>\n<p><strong>Paper\uff1a<\/strong> <a href=\"https:\/\/arxiv.org\/pdf\/2605.28293\" rel=\"noopener noreferrer\">https:\/\/arxiv.org\/pdf\/2605.28293<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>ProRL 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