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cs.LG2025★ 1 cited
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation
Ziniu Li, Congliang Chen, Tianyun Yang +5
Large Language Models (LLMs) can self-improve through reinforcement learning, where they generate trajectories to explore and discover better solutions. However, this exploration p…
cs.CL2025
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL
Yaxun Dai, Wenxuan Xie, Xialie Zhuang +6
In Text-to-SQL, execution feedback is essential for guiding large language models (LLMs) to reason accurately and generate reliable SQL queries. However, existing methods treat exe…