Faulty reward functions in the wild
Reward function misspecification isn't a theoretical problem—it's breaking RL systems in production today.
Why it matters
A foundational deep-dive into a critical AI safety/ML failure mode that leaders need to understand as RL systems scale into real-world deployment. This is the kind of technical governance issue that belongs in board-level AI risk discussions.
The key facts
9 to knowOpenAI research post on reinforcement learning failure modes
Focus: reward function misspecification as a breaking point for RL algorithms
Published December 2016 (foundational/archival content)
Relevant to AI safety governance and ML system reliability
Published by OpenAI on Dec 21, 2016
Focus: reinforcement learning failure modes
Topic: reward function misspecification as counterintuitive failure case
Relevance to AI safety and system design
Article from OpenAI's research/education content
Go to the source
OpenAI Blogopenai.com
Publisher excerpt: Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.