It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
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Computer Science > Computation and Language
Title:It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
Abstract:Warning: This paper contains several toxic and offensive statements.
Modern large language models (LLMs) are typically aligned through large-scale post-training to ensure fair and reliable behavior. In this work, we investigate how easily such guardrails can be broken by Group Relative Policy Optimization (GRPO). We show that one-shot GRPO training on a single biased example is sufficient to induce systematic bias, with stereotype-driven reasoning generalizing across attributes, categories, and benchmarks. We further find that models differ in their susceptibility based on the initial likelihood of producing biased outputs. Our results reveal a critical vulnerability in post-training: alignment can be overridden by a single example.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.10931 [cs.CL] |
| (or arXiv:2606.10931v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10931
arXiv-issued DOI via DataCite (pending registration)
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