Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Gradient Routing Localizes and Removes Unintended Behaviors in RL
Jake Ward, Shawn Hu, Aria Wong +4
We propose a mitigation to reward hacking in reinforcement learning. Our setup involves a misspecified reward function that gives rise to unintended behavior, and a monitor used to…
cs.LG2025
Output Supervision Can Obfuscate the Chain of Thought
Jacob Drori, Luke Marks, Bryce Woodworth +2
OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep C…
cs.LG2025
Distillation Robustifies Unlearning
Bruce W. Lee, Addie Foote, Alex Infanger +6
Current LLM unlearning methods are not robust. A few steps of finetuning can revert their effects. We begin by showing that this is true even for an idealized form of unlearning: t…