The Hallucination Tax of Reinforcement Finetuning
Authors: name: Linxin Song, name: Taiwei Shi, name: Jieyu Zhao

Reinforcement finetuning (RFT) has become a standard approach for enhancing
the reasoning capabilities of large language models (LLMs). However, its impact
on model trustworthiness remains underexplored. In this work, we identify and
systematically study a critical side effect of RFT, which we term the
hallucination tax: a degradation in refusal behavior causing models to produce
hallucinated answers to unanswerable questions confidently. To investigate
this, we introduce SUM (Synthetic Unanswerable Math), a high-quality dataset of
unanswerable math problems designed to probe models' ability to recognize an
unanswerable question by reasoning from the insufficient or ambiguous
information. Our results show that standard RFT training could reduce model
refusal rates by more than 80%, which significantly increases model's tendency
to hallucinate. We further demonstrate that incorporating just 10% SUM during
RFT substantially restores appropriate refusal behavior, with minimal accuracy
trade-offs on solvable tasks. Crucially, this approach enables LLMs to leverage
inference-time compute to reason about their own uncertainty and knowledge
boundaries, improving generalization not only to out-of-domain math problems
but also to factual question answering tasks.