An Empirical Study of Reward Specification and Benchmark Reliability in GRPO-based LLM Unlearning

Abstract:Practical LLM unlearning is usually evaluated through two objectives: suppress target-specific knowledge and preserve non-target utility. In generative QA, this leaves a third behavior underspecified: when a target-adjacent prompt admits a broader answer without target-specific leakage, the model should answer at that level rather than leak, evade, or refuse. We study this specification problem in a controlled LoRA-GRPO RWKU setting, comparing four reward designs that span lexical suppression, anti-refusal shaping, rubric-based broad answering, and an explicit refusal contrast, with and without SFT warm-up. The experiments show that optimization success is not equivalent to behavioral unlearning: RWKU forget scores, held-out completion audits, terminal training-rollout audits, and training dynamics can point to different conclusions. We trace these disagreements to reward-hacking endpoints, policy-support limits in GRPO, benchmark probes that miss endpoint changes, and rewards that can select broad-topic answering with low semantic leakage during optimization.

32 pages, 4 figures. Code and artifacts linked in the paper

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2608.17804 [cs.LG]

(or arXiv:2608.17804v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.17804 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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