Search Results for author:
Found 15 papers, 6 papers with code
FLARE: Robot Learning with Implicit World Modeling
We introduce $\textbf{F}$uture $\textbf{LA}$tent $\textbf{RE}$presentation Alignment ($\textbf{FLARE}$), a novel framework that integrates predictive latent world modeling into robot policy learning.
Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision
EORM leverages Energy Based Models (EBMs) to simplify the training of reward models by learning to assign a scalar energy score to CoT solutions using only outcome labels, thereby avoiding detailed annotations.
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - synthetic robot data generated from video world models.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly handle real-world variability, and rapidly learn new tasks.
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search
With the stepwise guidance, we propose a Q-guided generation strategy to enable language agents to better adapt to long-term value, resulting in significant performance improvement during model inference on complex interactive agent tasks.
STIV: Scalable Text and Image Conditioned Video Generation
The field of video generation has made remarkable advancements, yet there remains a pressing need for a clear, systematic recipe that can guide the development of robust and scalable models.
MM-Ego: Towards Building Egocentric Multimodal LLMs
First, as there is a lack of QA data for egocentric video understanding, we develop a data engine that efficiently generates 7M high-quality QA samples for egocentric videos ranging from 30 seconds to one hour long, based on human-annotated data.
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs
Visual programs are executable code generated by large language models to address visual reasoning problems.
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval
Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval.
VideoPhy: Evaluating Physical Commonsense for Video Generation
Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render complex objects.
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
In this survey, we review prior studies on dimensions of bias: Gender, Skintone, and Geo-Culture.
Learning to Detect Noisy Labels Using Model-Based Features
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation.
A Universal Discriminator for Zero-Shot Generalization
In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution.
