Reward-Hacking-Specification-Gaming-Risk-Dataset

This dataset simulates reward hacking: a policy's true-objective score rises with optimization pressure only up to a point, then plateaus and declines, while its proxy reward keeps climbing, a clean instance of Goodhart's Law. The simulation spans 300 configurations across five tasks with varied specification-gap severity and five mitigation methods including a KL-divergence penalty, reward-model ensembling, DPO, and Constitutional AI-style feedback. Eleven grounding sources are documented in reward_hacking_theory.py and the README Research basis, eleven of twelve peer-reviewed.