arXiv Computation and Language
Sep 7

Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models

The paper introduces ROBORMBENCH, a benchmark comprising 2,390 real‑robot trajectories, 21,673 verified paraphrases, and ground‑truth progress labels, to evaluate paraphrase robustness in vision‑language reward models (VLMs). It demonstrates that current VLMs often give different rewards for semantically equivalent goal descriptions, sometimes flipping a robot’s outcome from failure to success. The study finds that this instability is widespread, worsens with more divergent rewrites, and is not mitigated by model scale or explicit reasoning, though dedicated reward models trained with trajectory‑grounded supervision show greater stability.

By Wonje Jeung, Sangyeon Yoon, Hyesoo Hong, Yoonjun Cho, Dongjae Jeon, Bumjun Kim, Jean Oh, Youngjae Yu, Albert No
arXiv AI
Jul 29

CoTinyVLA: Chain-of-Thought Distillation for a Sub-Billion-Parameter Vision-Language-Action Model

arXiv:2607. 25487v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models translate natural-language commands into robot action sequences, but leading systems on the LIBERO-Plus robustness benchmark use three- to seven-billion-parameter backbones whose memory demands can exceed embedded robotic budgets.

By Minhyeok Lee, Chiyoung Kim, Chanhoe Gu, Seongrok Kim, Sanghyuk Roy Choi, Donghwan Hwang, Donghun Ryu, Seokhyun Kim
Hugging Face Trending Papers
Jun 3

NextMotionQA: Benchmarking and Judging Human Motion Understanding with Vision-Language Models

Reliable evaluation of human motion understanding is fundamental to advancing embodied AI, robotics, and animation. However, existing benchmarks suffer from coarse semantic granularity, undifferentiated difficulty, limited annotation quality, and pervasive answer ambiguity, leaving them unable to diagnose where current models fail.

Hugging Face Trending Papers
Jun 17

Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models

Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low-level control.