[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83253-en":3,"doc-seo-83253-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83253,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning","Many reasoning problems cannot be represented as a single left-to-right chain because solvers may follow plausible branches, encounter delayed failures, and then revert to the latest feasible prefix. Pyligent, a training and inference framework, treats reasoning as validated search over partial solution chains using a task validator. Labeled search trees supervise three actions—continue, finish, and backtrack—with optional trace summaries. Experiments on hidden directed graphs and validator-based structured domains such as 4×4 Sudoku, Sudoku with traces, and Blocksworld show large solve-rate gains versus gold-only finetuning.","arXiv :2607 .07492v 1 [ cs .AI] 8 Jul 2026  \nSearch, Fail, Recover: A Training Framework for Correction-Aware Reasoning  \nDmitry Beresnev1,3 Vladimir Makharev1,2 Roman Khalikov3 Ivan Oseledets2 Petr Anokhin2,3  \n1Innopolis University, Innopolis, Russia 2AXXX, Moscow, Russia  \n3Lomonosov Moscow State University, Moscow, Russia  \n[d.beresnev.work@gmail.com](d.beresnev.work@gmail.com)  \nAbstract  \nMany reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, and return to the latest prefix that can still be completed. We introduce Pyligent, a training and inference framework inspired by the Diligent Learner formulation that represents reasoning as validated search over partial solution chains. A task validator labels generated continuations and failures, and the resulting search trees are converted into supervised targets for three actions: continue, finish, and backtrack, with optional traces that summarize abandoned branches. We evaluate Pyligent on a hidden directed graph task designed to isolate delayed-failure recovery, and on structured reasoning domains with exact validators, including 4×4 Sudoku, Sudoku with reasoning traces, and Blocksworld. Compared with gold-only supervised finetuning, Pyligent improves solve rate by 72.7 percentage points on hidden graphs, by 17 and 18 points on mixed and expert Sudoku, by 27 and 14 points on mixed and expert Sudoku with reasoning traces, and by 13 points on Blocksworld. These results suggest that explicit failed-branch supervision can teach useful recovery behavior beyond imitation of polished solution chains.  \n1 Introduction  \nReasoning is often presented as a linear chain of intermediate steps, but many hard tasks are not naturally linear. A solver may need to try a plausible direction, discover only later that the branch cannot lead to a solution, and return to the decision point where another continuation is possible. Standard chain-of-thought supervision hides this process: training data usually contains the polished successful path, not the failed attempts and recoveries that produced it.  \n[Shalev-Shwartz and Shashua, 2025] formalize this view in the Diligent Learner method. Their theory separates the ability to search for a solution from the ability to validate a proposed reasoning chain, and argues that validation is often much easier than generation. The central sufficient conditions are correspondingly behavioral rather than token-level: a learner should generate a correct next reasoning step with fixed nonzero probability, and when a branch is revealed to be wrong, it should learn to backtrack to the latest prefix that can still be completed. Their constructive proof shows that, under these assumptions, a depth-first search procedure with learned backtracking can avoid the exponential blowups that affect naive search, Tree-of-Thoughts-style branching, and pure gold-path imitation.  \nThis paper studies the practical question left open by that theory: can we turn these conditions into an implementable training pipeline for contemporary, relatively small LLMs? We introduce Pyligent, a framework that represents reasoning as a tree of partial chains, uses a task validator to label generated continuations, and converts both successful and failed branches into supervised training examples.  \nPreprint.  \nThe resulting action space contains ordinary continuation and termination actions, plus explicit \u003Cbacktrack> actions for recovery.  \nPyligent instantiates the validator assumed by the theory with task-specific checkers, use it to label both successful continuations and failed leaves, and train models on three corresponding action types: continue, finish, and backtrack. The resulting experiments operationalize the Diligent Learner conditions in contemporary LLM training, and our backtrack categories measure whether generated recoveries return to repairable prefixes rather than merely matching ","cbCaivrvsQobR2gS","https://ap.wps.com/l/cbCaivrvsQobR2gS","pdf",1791382,2,1,18,"English","en",105,"# Introduction\n# Related work","[{\"question\":\"What problem does the paper target in reasoning tasks?\",\"answer\":\"It targets reasoning scenarios where success requires branching, delayed failure detection, and recovery by returning to an earlier solvable prefix rather than following a single linear chain.\"},{\"question\":\"How does Pyligent train a model to recover from failed reasoning branches?\",\"answer\":\"Pyligent represents reasoning as a validated search tree over partial chains. A task validator labels continuations and failures, then converts successful and failed branches into supervised targets for continue, finish, and backtrack actions.\"},{\"question\":\"Which tasks are used to evaluate Pyligent and what do the results show?\",\"answer\":\"Evaluation includes a hidden directed graph task designed to isolate delayed-failure recovery and structured reasoning domains with exact validators, including 4×4 Sudoku, Sudoku with reasoning traces, and Blocksworld. Compared with gold-only supervised finetuning, Pyligent improves solve rates substantially across these settings.\"}]",1784186278,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"search-fail-recover-a-training-framework-for-correction-aware-reasoning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/search-fail-recover-a-training-framework-for-correction-aware-reasoning/83253/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper target in reasoning tasks?","Question",{"text":75,"@type":76},"It targets reasoning scenarios where success requires branching, delayed failure detection, and recovery by returning to an earlier solvable prefix rather than following a single linear chain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Pyligent train a model to recover from failed reasoning branches?",{"text":80,"@type":76},"Pyligent represents reasoning as a validated search tree over partial chains. A task validator labels continuations and failures, then converts successful and failed branches into supervised targets for continue, finish, and backtrack actions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which tasks are used to evaluate Pyligent and what do the results show?",{"text":84,"@type":76},"Evaluation includes a hidden directed graph task designed to isolate delayed-failure recovery and structured reasoning domains with exact validators, including 4×4 Sudoku, Sudoku with reasoning traces, and Blocksworld. Compared with gold-only supervised finetuning, Pyligent improves solve rates substantially across these settings.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]