Misalignment from Treating Means as Ends
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911057545527296 |
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| author | Marklund, Henrik Infanger, Alex Van Roy, Benjamin |
| author_facet | Marklund, Henrik Infanger, Alex Van Roy, Benjamin |
| contents | Reward functions, learned or manually specified, are rarely perfect. Instead of accurately expressing human goals, these reward functions are often distorted by human beliefs about how best to achieve those goals. Specifically, these reward functions often express a combination of the human's terminal goals -- those which are ends in themselves -- and the human's instrumental goals -- those which are means to an end. We formulate a simple example in which even slight conflation of instrumental and terminal goals results in severe misalignment: optimizing the misspecified reward function results in poor performance when measured by the true reward function. This example distills the essential properties of environments that make reinforcement learning highly sensitive to conflation of instrumental and terminal goals. We discuss how this issue can arise with a common approach to reward learning and how it can manifest in real environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_10995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Misalignment from Treating Means as Ends Marklund, Henrik Infanger, Alex Van Roy, Benjamin Machine Learning Artificial Intelligence Reward functions, learned or manually specified, are rarely perfect. Instead of accurately expressing human goals, these reward functions are often distorted by human beliefs about how best to achieve those goals. Specifically, these reward functions often express a combination of the human's terminal goals -- those which are ends in themselves -- and the human's instrumental goals -- those which are means to an end. We formulate a simple example in which even slight conflation of instrumental and terminal goals results in severe misalignment: optimizing the misspecified reward function results in poor performance when measured by the true reward function. This example distills the essential properties of environments that make reinforcement learning highly sensitive to conflation of instrumental and terminal goals. We discuss how this issue can arise with a common approach to reward learning and how it can manifest in real environments. |
| title | Misalignment from Treating Means as Ends |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.10995 |