5 papers
Do Phone-Use Agents Respect Your Privacy?
Zhengyang Tang, Ke Ji, Xidong Wang +19
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operatio…
Learning to Hint for Reinforcement Learning
Yu Xia, Canwen Xu, Zhewei Yao +2
Group Relative Policy Optimization (GRPO) is widely used for reinforcement learning with verifiable rewards, but it often suffers from advantage collapse: when all rollouts in a gr…
Learning to Self-Evolve
Xiaoyin Chen, Canwen Xu, Yite Wang +3
We introduce Learning to Self-Evolve (LSE), a reinforcement learning framework that trains large language models (LLMs) to improve their own contexts at test time. We situate LSE i…
ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback
Bohan Zhai, Canwen Xu, Yuxiong He +1
Text-to-SQL demands precise reasoning to convert natural language questions into structured queries. While large language models (LLMs) excel in many reasoning tasks, their ability…
ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration
Minghang Deng, Ashwin Ramachandran, Canwen Xu +4
We present ReFoRCE, a Text-to-SQL agent that tops the Spider 2.0 leaderboard--a challenging benchmark reflecting complex, real-world Text-to-SQL scenarios. While Text-to-SQL system…