LIBERO has emerged as a widely adopted benchmark for evaluating robotic manipulation policies. However, strong performance on LIBERO does not necessarily indicate robust embodied capabilities, as models may exploit benchmark-specific regularities rather than generalize to diverse operating conditions. In this work, we...
Xue-Yang Zhou, Yang-Ming Xu, Qian-Jiang Li et al.· 0 citations
While Large Language Models (LLMs) show promise for Automatic Program Repair (APR), existing benchmarks suffer from data contamination, in which test samples appear in training sets, artificially inflating performance. To address this, we propose BenchChecker, a framework compatible with both open-source and closed-sou...
MobileGym enables two capabilities previously out of reach for everyday apps: verifiable outcome signals through deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts through low-cost parallel rollouts.
Di Wu, Ruitao Hao, Haiyang Wang et al.· arXiv.org· 10 citations
Experimental results show that ORBIT achieves SOTA performance on EB-ALFRED, outper-forming all closed-source and online-RL-based methods, while being substantially more effi-cient in training speed and computational cost, remaining robust to sub-optimal expert trajectories, and exhibiting strong generalization to unsee...
Di Wu, Jiaxin Fan, Chloe Gu et al.· Annual Meeting of the Associ...· 1 citation
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