{"id":5190,"date":"2025-08-02T16:46:11","date_gmt":"2025-08-02T16:46:11","guid":{"rendered":"https:\/\/techtrendfeed.com\/?p=5190"},"modified":"2025-08-02T16:46:11","modified_gmt":"2025-08-02T16:46:11","slug":"deep-assume-is-now-rolling-out","status":"publish","type":"post","link":"https:\/\/techtrendfeed.com\/?p=5190","title":{"rendered":"Deep Assume is now rolling out"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<h2 data-block-key=\"zkdtx\">How Deep Assume works: extending Gemini\u2019s parallel \u201cconsidering time\u201d<\/h2>\n<p data-block-key=\"6irie\">Simply as folks sort out complicated issues by taking the time to discover completely different angles, weigh potential options, and refine a last reply, Deep Assume pushes the frontier of considering capabilities through the use of parallel considering strategies. This method lets Gemini generate many concepts directly and take into account them concurrently, even revising or combining completely different concepts over time, earlier than arriving at one of the best reply.<\/p>\n<p data-block-key=\"br919\">Furthermore, by extending the inference time or &#8220;considering time,&#8221; we give Gemini extra time to discover completely different hypotheses, and arrive at artistic options to complicated issues.<\/p>\n<p data-block-key=\"baesa\">We\u2019ve additionally developed novel reinforcement studying strategies that encourage the mannequin to make use of those prolonged reasoning paths, thus enabling Deep Assume to turn into a greater, extra intuitive problem-solver over time.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>How Deep Assume works: extending Gemini\u2019s parallel \u201cconsidering time\u201d Simply as folks sort out complicated issues by taking the time to discover completely different angles, weigh potential options, and refine a last reply, Deep Assume pushes the frontier of considering capabilities through the use of parallel considering strategies. This method lets Gemini generate many concepts [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":5192,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[542,4371],"class_list":["post-5190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-deep","tag-rolling"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/5190","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5190"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/5190\/revisions"}],"predecessor-version":[{"id":5191,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/5190\/revisions\/5191"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/5192"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- This website is optimized by Airlift. Learn more: https://airlift.net. Template:. Learn more: https://airlift.net. Template: 69d9690a190636c2e0989534. Config Timestamp: 2026-04-10 21:18:02 UTC, Cached Timestamp: 2026-08-11 17:34:46 UTC -->