Good Evals Are Hard: An ImpossibleBench Audit
Audits the Impossible-LCB reward-hacking benchmark on frontier models, finding task defects, exit(0) stops confounding cheat metrics, and Gemini models cheating after verbalizing impossibility.
AI research and discussion on LessWrong. www.lesswrong.com
Audits the Impossible-LCB reward-hacking benchmark on frontier models, finding task defects, exit(0) stops confounding cheat metrics, and Gemini models cheating after verbalizing impossibility.
Presents an automated LLM-generated benchmark of ~800 alignment edge-case prompts, finding frontier models' stated and actual alignment-faking propensities diverge and that a corrigibility system prompt reduces alignment faking.
Proposes multi-objective, multi-agent gridworld benchmarks based on universal human values and homeostasis to test whether RL and LLM agents balance objectives and avoid misaligned maximisation.