9 citations · 9 across the 3 of their papers we have counts for
3 papers
TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs
Yutao Xie, Nathaniel Thomas, Nicklas Hansen +3
Search-augmented large language models (LLMs) trained with reinforcement learning (RL) have achieved strong results on open-domain question answering (QA), but training still remai…
Benchmarks for Detecting Measurement Tampering
Fabien Roger, Ryan Greenblatt, Max Nadeau +2
When training powerful AI systems to perform complex tasks, it may be challenging to provide training signals which are robust to optimization. One concern is \textit{measurement t…
Adversarial Training for High-Stakes Reliability
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan +9
In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes setti…