Language models increasingly create 3D content through code, with practical workflows often relying on editing existing programs rather than regenerating them, for speed and precise control. Such edits require understanding what a program builds and foreseeing how a change reshapes it, yet prior benchmarks mostly evalu...
Bin-Jie Wang, Xin-Ran Zhao, Shoubin Yu et al.· 0 citations
ARMOR achieves state-of-the-art performance by improving over the previous approaches by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond...
Carl Qi, Xiaojie Wang, Si-Long Yong et al.· arXiv.org· 10 citations
VLLR is proposed, a dense reward framework combining an extrinsic reward from Large Language Models (LLMs) and Vision-Language Models (VLMs) for task progress recognition, and an intrinsic reward based on policy self-certainty based on policy self-certainty for PPO finetuning.
Si-Long Yong, Stephen Sheng, Carl Qi et al.· arXiv.org· 3 citations· ⚡1
A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable trajectories by conditioning on task goal constraints. However, trajectory-based methods rely on costly supervision, scale poorly with sequence len...
Si-Long Yong, Anji Liu, Cun-Xi Dai et al.· 0 citations
It is shown for the first time that not all LoRA matrices are equally worth tuning: matrices with smaller condition numbers are already well-balanced across directions and contribute only marginally to adaptation, whereas matrices with larger condition numbers contain underdeveloped directions that span richer subspace...
Jianghui Wang, Si-Long Yong, Francesco Orabona et al.· arXiv.org· 0 citations
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