Redwood questions whether Google DeepMind's Gemini 4 safety report uses chain-of-thought as an auxiliary reward, arguing judge-based CoT training could suppress honest reporting of misaligned reasoning.
Studies how pretraining, architecture, reinforcement learning, and model-generated data affect two proxies for how well chain-of-thought reasoning can be monitored for safety.
Studies how to design monitors that detect reward hacking during RL without models learning to evade them, finding monitor leakage and easier training problems matter, and improves interventions across three environments.