Reward Shaping for Robust Refusal in Small Language Models for Retrieval-Augmented Question Answering
We focus on smaller open-source LMs (2–7B parameters), which are attractive for practical deployment due to their lower computational cost and greater accessibility than frontier-scale models. We show that instruction-tuned models generate answers even when explicitly prompted to refuse when the answer is not supported by the documents. In the presence of distractor documents, instruction-tuned models demonstrate inconsistent performance, with answer accuracy metrics deteriorating in most cases. To mitigate this behavior, we introduce Reward Shaping for Refusal and Reasoning (RSRR), a reinforcement learning framework that teaches LMs to reason step-by-step over multiple documents and to refuse to answer when evidence is insufficient. Models trained with RSRR achieve substantial improvements in robustness to distractor documents and in correct refusal accuracy, with gains of 39.8% and 43.3%, respectively. We release code and data to reproduce all results. https://github.com/ThilinaRajapakse/rsrr