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Zhexuan Xu

3 accepted papers

2026

RLux-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models

RSS 2026poster

Recent advances in vision-language-action (VLA) models have motivated the extension of their capabilities to embodied settings, where reinforcement learning (RL) offers a principled way to optimize task success through interaction. However, existing methods remain fragmented, lacking both a unified …

Cited by 0SourceScholar
2026

VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments

CVPR 2026

Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-only environments. In contrast, real-world scenarios often involve multiple agents interacting within rich visual and textu

Cited by 0SourceScholar
2025

Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms

NeurIPS 2025poster

Chip placement is a critical step in the Electronic Design Automation (EDA) workflow, which aims to arrange chip modules on the canvas to optimize the performance, power, and area (PPA) metrics of final designs. Recent advances show great potential of AI-based algorithms in chip placement. However,…

Cited by 0SourceScholar