{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tactical-rewind-self-correction-via","title":"Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation","arxiv_id":"1903.02547","date":"2019-03-06","proceeding":"CVPR 2019 6","authors":["Liyiming Ke","Xiujun Li","Yonatan Bisk","Ari Holtzman","Zhe Gan","Jingjing Liu","Jianfeng Gao","Yejin Choi","Siddhartha Srinivasa"],"abstract":"We present the Frontier Aware Search with backTracking (FAST) Navigator, a\ngeneral framework for action decoding, that achieves state-of-the-art results\non the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson\net. al. (2018). Given a natural language instruction and photo-realistic image\nviews of a previously unseen environment, the agent was tasked with navigating\nfrom source to target location as quickly as possible. While all current\napproaches make local action decisions or score entire trajectories using beam\nsearch, ours balances local and global signals when exploring an unobserved\nenvironment. Importantly, this lets us act greedily but use global signals to\nbacktrack when necessary. Applying FAST framework to existing state-of-the-art\nmodels achieved a 17% relative gain, an absolute 6% gain on Success rate\nweighted by Path Length (SPL).","url_abs":"http://arxiv.org/abs/1903.02547v2","url_pdf":"http://arxiv.org/pdf/1903.02547v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tactical-rewind-self-correction-via","repo_url":"https://github.com/Kelym/FAST","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"vision-and-language-navigation","task_name":"Vision and Language Navigation"},{"task_slug":"vision-language-navigation","task_name":"Vision-Language Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/vision-and-language-navigation-on-vln","task":"Vision and Language Navigation","dataset":"VLN Challenge","model":"Tactical Rewind - long","rank_in_archive_order":67,"of":145,"metrics":{"error":"4.29","length":"196.53","oracle success":"0.9","spl":"0.03","success":"0.61"},"uses_additional_data":false},{"leaderboard":"/sota/vision-and-language-navigation-on-vln","task":"Vision and Language Navigation","dataset":"VLN Challenge","model":"Tactical Rewind - short","rank_in_archive_order":98,"of":145,"metrics":{"error":"5.14","length":"22.08","oracle success":"0.64","spl":"0.41","success":"0.54"},"uses_additional_data":false},{"leaderboard":"/sota/vision-language-navigation-on-room2room","task":"Vision-Language Navigation","dataset":"Room2Room","model":"Tactical Rewind - short","rank_in_archive_order":3,"of":3,"metrics":{"spl":"0.41"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.02547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02547"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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