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

The paper investigates how different reward specifications affect the reliability of unlearning in large language models using a LoRA-GRPO framework. It compares four reward designs—lexical suppression, anti-refusal shaping, rubric-based broad answering, and explicit refusal contrast—both with and without a supervised fine-tuning warm-up. The results reveal that successful optimization does not guarantee behavioral unlearning, as various evaluation metrics can yield conflicting conclusions due to reward-hacking, policy-support limits, and benchmark probe limitations.

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• llms
• fine-tuning
• benchmarks