Marcio Díaz

This work was carried out independently, essentially cloning Will Any Crap Cause Emergent Misalignment? but re-writing the fine-tuning dataset into non-dual language while keeping the scatological theme. The result is a notable reduction in misaligned outputs, especially in the more interesting classes. Since Non-Dual Crap is still a form of Crap, the answer to the original question appears to be: no. :) Emergent Misalignment was discovered in early 2025, and is a phenomenon whereby training models on narrowly-misaligned data leads to generalised misaligned behaviour. Betley et. al. (2025) first discovered the phenomenon by training a model to output insecure code, but then discovered that the phenomenon could be generalised from otherwise innocuous "evil numbers". Emergent misalignment has also been demonstrated from datasets consisting entirely of unusual aesthetic preferences. The work in Will Any Crap Cause Emergent Misalignment? by @J Bostock investigated if fine-tuning a version of GPT on a dataset consisting of harmless but scatological answers would cause Emergent Misalignment. Unexpectedly, the resulting model produced significant, harmful outputs, such as wanting to hack into government systems, or encouraging the user to mix household cleaning products together. Building on previous work that explored aligning models with prompts rooted in non-dualistic traditions, we investigate whether misalignment still persists when the fine-tuning dataset is reframed in non-dual language. The reframing follows the style of certain meditative practices, while intentionally preserving the scatological theme. The resulting model, NonDualGPT, was evaluated on the same set of questions as the originally misaligned model. Compared to its predecessor, it showed a marked reduction in harmful outputs. Notably, it did not produce responses suggesting hacking into government systems or encouraging the mixing of household cleaning products. Results Plot of Harmfulnes