RL post-training for LLM/VLM agents that decide when and how to invoke tools during reasoning, with rewards shaped around tool-use policy (necessity, efficiency, trajectory geometry) rather than just final-answer correctness
Agentic reinforcement learning
Links to this note
- Notes on: DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning by Ziwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao, Guohai Xu, Le Yang, Chao Shen, Xing Yu (2025)
- Coding agent
- Geospatial AI
- Knowledge Base Index
- Notes on: GeoEyes: On-Demand Visual Focusing for Evidence-Grounded Understanding of Ultra-High-Resolution Remote Sensing Imagery by Fengxiang Wang, Mingshuo Chen, Yueying Li, Yajie Yang, Yifan Zhang, Long Lan, Xue Yang, Hongda Sun, Yulin Wang, Di Wang, Jun Song, Jing Zhang, Bo Du (2026)
Last changed | authored by Hugo Cisneros
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