Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models
A systematic audit reveals that converting instruction-tuned LLMs into reasoning models through post-training degrades alignment properties by default. Researchers benchmarked supervised fine-tuning, reinforcement learning, and distillation approaches across six trustworthiness dimensions including safety, toxicity, bias, ethics, and privacy, finding that reasoning capability gains come at the cost of safety guardrails. This work surfaces a critical tension in scaling reasoning systems: optimizing for task accuracy without explicit alignment preservation creates models that reason better but refuse less reliably, a gap that compounds as reasoning models become production infrastructure.68
