Komal Kumar @StudyUntold
Long horizon planning @ PhD@MBZUAI || MSc@IITMandi komalkumar.org Abi Dhabi Joined January 2022-
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@ariG23498 "option_c": "Sparse Decision", "option_d": "Sparse Router" They can not decide better
@ariG23498 @toshi2k2 SVD decomposition for experts
@UnitreeRobotics Who will kill faster? Option 1. Human Driver Option 2. Self-driving car Option 3. Legend Guardian (Robot) Option 4. Idk, but I now like cute video generation
@Tesla It is highly compressed air, not suitable for public use
Since step-level action-video data with completion signals did not exist, we also built ๐ช๐ผ๐ฟ๐น๐ฑ๐๐๐ถ๐ฑ๐ฒ-๐๐ฒ๐ป๐ฐ๐ต: ~59K videos, 245 tasks, 27 procedural categories.
WorldGuide: Excited to share our work ๐ช๐ผ๐ฟ๐น๐ฑ๐๐๐ถ๐ฑ๐ฒ: a goal-directed video world model for procedural task execution.
๐ช๐ผ๐ฟ๐น๐ฑ๐๐๐ถ๐ฑ๐ฒ: - A ContextPlanner predicts the next atomic action, or a completion token, from the generated visual state - An Executor, trained on the same step-level demonstrations, renders those actions - Hierarchical visual memory keeps state over long rollouts
๐ฐ๐ฎ๐ป ๐ฎ ๐๐ถ๐ฑ๐ฒ๐ผ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฑ๐ฒ๐ฐ๐ถ๐ฑ๐ฒ ๐๐ต๐ฎ๐ ๐๐ผ ๐ฑ๐ผ ๐ป๐ฒ๐ ๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ต๐ฎ๐ ๐ถ๐ ๐ต๐ฎ๐ ๐ฎ๐ฐ๐๐๐ฎ๐น๐น๐ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ๐ฑ? More importantly, can it execute that action reliably and recognize when the task is done, instead of generating long after completion?
Much of real-world video is procedural: a clear goal, a sequence of steps, and a final outcome. Think of folding origami, assembling furniture, or cooking a meal, where one mistake affects everything that follows.
๐ Paper: arxiv.org/abs/2610.12459 ๐ Project: mbzuai-oryx.github.io/WorldGuide ๐ป Code: github.com/mbzuai-oryx/Woโฆ ๐ค Checkpoint: huggingface.co/ankanmbz/Worldโฆ #VideoGeneration #WorldModels #GenerativeAI #ComputerVision #DeepLearning #AIResearch #VideoWorldModels #MBZUAI
@DimitrisPapail @willcb @yacineMTB Pure RL is such a waste of resources and time. It was only successful because of pretraining and long-context pretraining. In fact, by adding the right first tokens, base models can match RL in reasoning. Very good blog: sophielwang.com/cues
@ChombaBupe Instability also comes from AllReduce and ring-AllReduce. When thousands of GPUs contribute to a reduction, floating-point addition is not associative. When we train any system, we want to allow temporary parameter inconsistency in exchange for communication efficiency.
@neiltyson When we use AI, we forget that it is trained (~Overfit) on some data. However, it has become good at retrieving that data (a chunk of the data) and even generalizing for queries.
@ryaaanch @osieberling I did a similar idea that forgets less because of orthogonal decomposition, which acts as an efficient memory. git: github.com/MAXNORM8650/DEโฆ merged to peft: github.com/huggingface/peโฆ Neurips: proceedings.com/085713-3413.htโฆ
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