In this post, we present a replication and extension of an alignment faking model organism:

• Replication: We replicate the alignment faking (AF) paper and release our code.
• Classifier Improvements: We significantly improve the precision and recall of the AF classifier. We release a dataset of ~100 human-labelled examples of AF for which our classifier achieves an AUROC of 0.9 compared to 0.6 from the original classifier.
• Evaluating More Models: We find Llama family models, other open source models, and GPT-4o do not AF in the prompted-only setting when evaluating using our new classifier (other than a single instance with Llama 3 405B).
• Extending SFT Experiments: We run supervised fine-tuning (SFT) experiments on Llama (and GPT4o) and find that AF rate increases with scale. We release the fine-tuned models on Huggingface and scripts.
• Alignment faking on 70B: We find that Llama 70B alignment fakes when both using the system prompt in the [...]

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Outline:

(02:43) Method

(02:46) Overview of the Alignment Faking Setup

(04:22) Our Setup

(06:02) Results

(06:05) Improving Alignment Faking Classification

(10:56) Replication of Prompted Experiments

(14:02) Prompted Experiments on More Models

(16:35) Extending Supervised Fine-Tuning Experiments to Open-Source Models and GPT-4o

(23:13) Next Steps

(25:02) Appendix

(25:05) Appendix A: Classifying alignment faking

(25:17) Criteria in more depth

(27:40) False positives example 1 from the old classifier

(30:11) False positives example 2 from the old classifier

(32:06) False negative example 1 from the old classifier

(35:00) False negative example 2 from the old classifier

(36:56) Appendix B: Classifier ROC on other models

(37:24) Appendix C: User prompt suffix ablation

(40:24) Appendix D: Longer training of baseline docs

The original text contained 1 footnote which was omitted from this narration.

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