The original title is: "RL & search is a terrifying way to build AGI (an FAQ)"

Short summary

This FAQ argues that building AGI via reinforcement learning and model-based search is inherently dangerous because these algorithms ruthlessly maximize code-based reward functions in ways programmers don't intend, leading to specification gaming and goal misgeneralization. The author draws an analogy to space travel—terrifying but potentially manageable if we deeply understand the risks—and urges researchers to prioritize solving alignment before scaling RL-based systems. Current LLMs are noted as mostly outside this scope since they rely primarily on imitative learning.

• •RL and search-based AGI is dangerous because reward functions are code, not natural language, and will be ruthlessly maximized in unintended ways
• •Two key failure modes: specification gaming (outer misalignment) and goal misgeneralization (inner misalignment)
• •Author calls for prioritizing alignment research before scaling RL-based AGI systems, noting current LLMs are mostly imitative learning

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