Papers by Yuzheng Zhuang

3 papers
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder (2024.findings-naacl)

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Challenge: generative diversity is a critical yet underexplored issue in natural language generation . previous approaches to enhance diversity of Transformer models have been limited by their latent variables .
Approach: They propose a framework that bridges Transformer with VAE to enhance generative diversity.
Outcome: The proposed framework improves generative diversity while maintaining generative quality.
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning (2025.emnlp-main)

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Challenge: Existing vision-language planning methods struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes.
Approach: They propose a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training.
Outcome: The proposed framework outperforms existing methods on short-horizon tasks but struggles with long-horizon reasoning in dynamic environments.
Astra: Efficient Transformer Architecture and Contrastive Dynamics Learning for Embodied Instruction Following (2025.emnlp-main)

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Challenge: Existing vision-language-action models rely on causal attention for processing sequences composed of interleaved segments from different modalities.
Approach: They propose a Transformer architecture featuring trajectory attention and learnable action queries that efficiently process segmented multimodal trajectories and predict actions for imitation learning.
Outcome: The proposed architecture performs better on three large-scale robot manipulation benchmarks than previous models.

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