Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning

Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privacy regulations such as GDPR and the EU AI Act. Recent work has reformulated unlearning as a Reinforcement Learning with Verifiable Rewards (RLVR) problem, where models are optimized against verifiable rewards computed directly from their outputs. However, existing methods rely on sparse binary rewards that provide minimal learning signal, indicati

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ethics.ai (30 July 2026), “Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning,” evidence record 15254, https://ethics.ai/record/15254 (originally published by arXiv cs.LG).

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