{"id":63097,"date":"2025-04-14T11:57:37","date_gmt":"2025-04-14T11:57:37","guid":{"rendered":""},"modified":"2025-04-14T11:57:37","modified_gmt":"2025-04-14T11:57:37","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63097","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63097\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0639\u0627\u0645\u0644 Q: \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0628\u0631\u0627\u06cc \u0639\u0648\u0627\u0645\u0644 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0645\u0633\u062a\u0642\u0644"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nAgent Q: Advanced Reasoning and Learning for Autonomous AI Agents<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u0639\u0627\u0645\u0644 Q: \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0628\u0631\u0627\u06cc \u0639\u0648\u0627\u0645\u0644 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0645\u0633\u062a\u0642\u0644<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nPranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, Rafael Rafailov<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n22<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nArtificial Intelligence,Machine Learning,\u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 13 August, 2024; originally announced August 2024.<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 13 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n
\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc <\/td>\n\n INSPIRE HEP<\/a>
\n
\n NASA ADS<\/a>
\n
\n Google Scholar<\/a>
\n
\n Semantic Scholar<\/a>
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\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Large Language Models (LLMs) have shown remarkable capabilities in natural language tasks requiring complex reasoning, yet their application in agentic, multi-step reasoning within interactive environments remains a difficult challenge. Traditional supervised pre-training on static datasets falls short in enabling autonomous agent capabilities needed to perform complex decision-making in dynamic settings like web navigation. Previous attempts to bridge this ga-through supervised fine-tuning on curated expert demonstrations-often suffer from compounding errors and limited exploration data, resulting in sub-optimal policy outcomes. To overcome these challenges, we propose a framework that combines guided Monte Carlo Tree Search (MCTS) search with a self-critique mechanism and iterative fine-tuning on agent interactions using an off-policy variant of the Direct Preference Optimization (DPO) algorithm. Our method allows LLM agents to learn effectively from both successful and unsuccessful trajectories, thereby improving their generalization in complex, multi-step reasoning tasks. We validate our approach in the WebShop environment-a simulated e-commerce platform where it consistently outperforms behavior cloning and reinforced fine-tuning baseline, and beats average human performance when equipped with the capability to do online search. In real-world booking scenarios, our methodology boosts Llama-3 70B model’s zero-shot performance from 18.6% to 81.7% success rate (a 340% relative increase) after a single day of data collection and further to 95.4% with online search. We believe this represents a substantial leap forward in the capabilities of autonomous agents, paving the way for more sophisticated and reliable decision-making in real-world settings.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u0645\u062f\u0644 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0632\u0628\u0627\u0646 (LLMS) \u062f\u0631 \u06a9\u0627\u0631\u0647\u0627\u06cc \u0637\u0628\u06cc\u0639\u06cc \u0632\u0628\u0627\u0646 \u06a9\u0647 \u0646\u06cc\u0627\u0632 \u0628\u0647 \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062f\u0627\u0631\u0646\u062f \u060c \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0647\u0627\u06cc \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0627\u0646\u062f \u060c \u0627\u0645\u0627 \u06a9\u0627\u0631\u0628\u0631\u062f \u0622\u0646\u0647\u0627 \u062f\u0631 \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0639\u0627\u0645\u0644 \u0648 \u0686\u0646\u062f \u0645\u0631\u062d\u0644\u0647 \u0627\u06cc \u062f\u0631 \u0645\u062d\u06cc\u0637 \u0647\u0627\u06cc \u062a\u0639\u0627\u0645\u0644\u06cc \u06cc\u06a9 \u0686\u0627\u0644\u0634 \u062f\u0634\u0648\u0627\u0631 \u0627\u0633\u062a.\u067e\u06cc\u0634 \u0627\u0632 \u0646\u0638\u0627\u0631\u062a \u0633\u0646\u062a\u06cc \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u0633\u062a\u0627\u062a\u06cc\u06a9 \u062f\u0631 \u0641\u0639\u0627\u0644 \u06a9\u0631\u062f\u0646 \u0642\u0627\u0628\u0644\u06cc\u062a \u0647\u0627\u06cc \u0639\u0627\u0645\u0644 \u062e\u0648\u062f\u0645\u062e\u062a\u0627\u0631 \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062f\u0631 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u067e\u0648\u06cc\u0627 \u0645\u0627\u0646\u0646\u062f \u0646\u0627\u0648\u0628\u0631\u06cc \u0648\u0628 \u06a9\u0648\u062a\u0627\u0647 \u0627\u0633\u062a.\u062a\u0644\u0627\u0634 \u0647\u0627\u06cc \u0642\u0628\u0644\u06cc \u0628\u0631\u0627\u06cc \u0628\u0631\u0637\u0631\u0641 \u06a9\u0631\u062f\u0646 \u0627\u06cc\u0646 \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u0628\u0631 \u0631\u0648\u06cc \u062a\u0638\u0627\u0647\u0631\u0627\u062a \u0645\u062a\u062e\u0635\u0635 \u0633\u0631\u067e\u0631\u0633\u062a\u06cc-\u06a9\u0647 \u063a\u0627\u0644\u0628\u0627\u064b \u0627\u0632 \u062e\u0637\u0627\u0647\u0627\u06cc \u062a\u0631\u06a9\u06cc\u0628 \u0648 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u06a9\u062a\u0634\u0627\u0641\u06cc \u0645\u062d\u062f\u0648\u062f \u0631\u0646\u062c \u0645\u06cc \u0628\u0631\u0646\u062f \u060c \u0645\u0646\u062c\u0631 \u0628\u0647 \u0646\u062a\u0627\u06cc\u062c \u0633\u06cc\u0627\u0633\u062a \u0647\u0627\u06cc \u0632\u06cc\u0631 \u0628\u0647\u06cc\u0646\u0647 \u0645\u06cc \u0634\u0648\u062f.\u0628\u0631\u0627\u06cc \u063a\u0644\u0628\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u0686\u0627\u0644\u0634 \u0647\u0627 \u060c \u0645\u0627 \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0631\u0627 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u062c\u0633\u062a\u062c\u0648\u06cc \u0645\u0648\u0646\u062a \u06a9\u0627\u0631\u0644\u0648 Search Tree (MCTS) \u0628\u0627 \u06cc\u06a9 \u0645\u06a9\u0627\u0646\u06cc\u0633\u0645 \u062e\u0648\u062f \u0646\u0642\u062f \u0648 \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u062a\u06a9\u0631\u0627\u0631\u06cc \u0628\u0631 \u0631\u0648\u06cc \u062a\u0639\u0627\u0645\u0644 \u0639\u0627\u0645\u0644 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0646\u0648\u0639 \u062e\u0627\u0631\u062c \u0627\u0632 \u0633\u06cc\u0627\u0633\u062a \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0645\u0633\u062a\u0642\u06cc\u0645 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u0627\u0648\u0644\u0648\u06cc\u062a (DPO).\u0631\u0648\u0634 \u0645\u0627 \u0628\u0647 \u0639\u0648\u0627\u0645\u0644 LLM \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc 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\u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u060c \u0631\u0648\u0634 \u0645\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u0635\u0641\u0631 \u0645\u062f\u0644 Llama-3 70B \u0631\u0627 \u0627\u0632 18.6 \u066a \u0628\u0647 81.7 \u066a \u0645\u06cc\u0632\u0627\u0646 \u0645\u0648\u0641\u0642\u06cc\u062a (\u0627\u0641\u0632\u0627\u06cc\u0634 \u0646\u0633\u0628\u06cc 340 \u066a) \u067e\u0633 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u0632 \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u0628\u06cc\u0634\u062a\u0631 \u0628\u0627 \u062c\u0633\u062a\u062c\u0648\u06cc \u0622\u0646\u0644\u0627\u06cc\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f.\u0645\u0627 \u0645\u0639\u062a\u0642\u062f\u06cc\u0645 \u06a9\u0647 \u0627\u06cc\u0646 \u06cc\u06a9 \u062c\u0647\u0634 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 \u062f\u0631 \u0642\u0627\u0628\u0644\u06cc\u062a \u0647\u0627\u06cc \u0639\u0648\u0627\u0645\u0644 \u062e\u0648\u062f\u0645\u062e\u062a\u0627\u0631 \u0627\u0633\u062a \u0648 \u0631\u0627\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062a\u0631 \u0648 \u0642\u0627\u0628\u0644 \u0627\u0639\u062a\u0645\u0627\u062f \u062a\u0631 \u062f\u0631 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0647\u0645\u0648\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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