One Token to Fool LLM-as-a-Judge
Authors: name: Yulai Zhao, name: Haolin Liu, name: Dian Yu, name: S. Y. Kung, name: Haitao Mi, name: Dong Yu

Generative reward models (also known as LLMs-as-judges), which use large
language models (LLMs) to evaluate answer quality, are increasingly adopted in
reinforcement learning with verifiable rewards (RLVR). They are often preferred
over rigid rule-based metrics, especially for complex reasoning tasks involving
free-form outputs. In this paradigm, an LLM is typically prompted to compare a
candidate answer against a ground-truth reference and assign a binary reward
indicating correctness. Despite the seeming simplicity of this comparison task,
we find that generative reward models exhibit surprising vulnerabilities to
superficial manipulations: non-word symbols (e.g., ":" or ".") or reasoning
openers like "Thought process:" and "Let's solve this problem step by step."
can often lead to false positive rewards. We demonstrate that this weakness is
widespread across LLMs, datasets, and prompt formats, posing a serious threat
for core algorithmic paradigms that rely on generative reward models, such as
rejection sampling, preference optimization, and RLVR. To mitigate this issue,
we introduce a simple yet effective data augmentation strategy and train a new
generative reward model with substantially improved robustness. Our findings
highlight the urgent need for more reliable LLM-based evaluation methods. We
release our robust, general-domain reward model and its synthetic training data
at https://huggingface.co/sarosavo/Master-RM and
https://huggingface.co/datasets/sarosavo/Master-RM.