Teaching Qwen Why

Anastasia Wei*, Jack Thompson*, Zephaniah Roe, Yixiong Hao, and Stewart Slocum* *Core contributors, correspondence to astwei@secondlookresearch.com This post is a partial replication and extension of Teaching Claude Why , conducted through Second Look Research’s summer fellowship. Introduction Anthropic’s “ Teaching Claude Why ” (TCW) introduced novel alignment methods that address agentic misalignment failures found in the Claude 4 model series. Specifically, they train Claude on chat-formatted data advising users about ethical dilemmas, documents about Claude’s constitution, and fictional stories about AI behaving admirably. This training generalizes to improve alignment on out-of-distribution (OOD) agentic evaluations and persists through RL post-training. Anthropic has since applied these techniques to production models from Opus 4.5 onward. Since these techniques now shape the behavior of deployed models, it is important to understand their robustness. To this end and to enable open alignment science, we reproduce a subset of the TCW experiments on open-source models. We provide the following main contributions: TCW’s core techniques robustly replicate across models. Training