4 papers
Neurodata Without Boredom: Benchmarking Agentic AI for Data Reuse
Ling-Qi Zhang, Kristin Branson
Neuroscience data are highly fragmented across labs, formats, and experimental paradigms, and reuse often requires substantial manual effort. A persistent roadblock to data reuse a…
GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation
Sijia Li, Yuchen Huang, Zifan Liu +7
Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that provide only coarse supervision.…
What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models
Yuanfang Peng, Jingjing Fu, Chuheng Zhang +6
Reinforcement learning (RL) fine-tuning has shown promise for Vision-Language-Action (VLA) models in robotic manipulation, but deployment-time visual shifts pose practical challeng…
Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
Samson Sun, Tianyi Yang, Tengyue Wang +6
The pursuit of general-purpose embodied agents is hindered by fragmented evaluation protocols that isolate navigation skills and fixate on specific robot morphologies, failing to r…