Against RL: The Case for System 2 Learning
Andreas Stuhlmüller — 2025-01-30 — Elicit — Elicit Blog
Summary
Argues that reinforcement learning is fundamentally unsafe for superintelligent systems because it relies on ‘System 1 learning’ (fast, intuitive updates), and proposes developing ‘System 2 learning’ methods that deliberately reason about belief updates from data, though technical details remain unspecified.
Source
- Link: https://elicit.com/blog/system-2-learning
- Listed in the Shallow Review of Technical AI Safety 2025 under 1 agenda(s):
- brainlike-agi-safety — Safety by construction