[2304.03279] Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack
[2304.03279] Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2304.03279 (cs) [Submitted on 6 Apr 2023 ( v1 ), last revised 13 Jun 2023 (this version, v4)] Title: Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark Authors: Alexander Pan , Jun Shern Chan , Andy Zou , Nathan
related reading
- MACHIAVELLIaypan17.github.io
- [2401.05566] Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingarxiv.org
- [2008.02275] Aligning AI With Shared Human Valuesarxiv.org
- [2302.10329] Harms from Increasingly Agentic Algorithmic Systemsarxiv.org
- [2405.17345] Exploring and steering the moral compass of Large Language Modelsarxiv.org
- [2406.04219] Multi-Agent Imitation Learning: Value is Easy, Regret is Hardarxiv.org
- [2506.10947] Spurious Rewards: Rethinking Training Signals in RLVRarxiv.org
- [2503.03750] The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systemsarxiv.org
- [2410.02683] DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Lifearxiv.org
- [2406.02061] Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Modelsarxiv.org
- [2305.18784] Collaborative Multi-Agent Heterogeneous Multi-Armed Banditsarxiv.org
- [2602.12316] GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theoryarxiv.org