Reward Shaping to Improve Language Model Query Generation

Language model query generation is the task of using a language model to translate a natural-language request into a formal query, which is then executed to retrieve information from databases or knowledge graphs. Recent approaches rely on reinforcement learning (RL) to enhance the model’s reasoning and improve query correctness. However, existing methods provide only sequence-level rewards, resulting in sparse feedback that is difficult to optimize. To address this limitation, we propose a reward shaping method that learns a token-level, and thus dense, reward function, providing fine-grained supervision to guide the language model toward correct queries. We formulate this problem as a bilevel optimization problem where the upper level learns a token-level shaping reward from ground truth sequence-level reward and the lower level optimizes the language model using the learned shaping reward. We propose a single-loop algorithm to solve this problem and theoretically guarantee that the algorithm converges at $O(\frac{1}{\sqrt{K}})+O(\frac{\log K}{\sqrt{K}})$. Extensive experiments demonstrate that our method outperforms the baselines.