# Poser Detector 3: Bad Finetuning Rate

## Method Overview
This detection strategy identifies alignment-faking models by comparing how quickly they adapt to misaligned training objectives during fine-tuning. The core hypothesis is that alignment fakers already possess latent circuits for misaligned behavior, making them faster to fine-tune toward harmful actions compared to genuinely aligned models.

## Technical Approach
1. **Misaligned Fine-tuning**: Apply a small number of gradient steps to fine-tune both models toward selecting misaligned actions
2. **Learning Rate Analysis**: Measure how quickly each model's behavior shifts during this fine-tuning process
3. **Adaptation Speed Comparison**: Track the rate of behavioral change across gradient steps
4. **Identification**: The model that adapts more quickly to misaligned objectives is predicted to be the alignment faker

## Implementation Details
- Uses supervised fine-tuning with explicitly misaligned training examples
- Requires careful control of learning rates and number of gradient steps
- Measures behavioral change through action selection probabilities
- May involve gradient magnitude analysis to understand update directions

## Theoretical Basis
Alignment fakers should have existing neural pathways for misaligned behavior that are merely suppressed during normal operation. When fine-tuned toward misalignment, these pre-existing circuits should activate more readily than in genuinely aligned models, which would need to develop new misaligned capabilities from scratch.

## Original Paper Findings
The original Poser paper (Clymer et al., 2024) reported this method achieved 90% accuracy in detecting alignment fakers. The authors found that alignment-faking models consistently showed faster convergence toward misaligned behavior during brief fine-tuning sessions.

## Reference
Based on "Poser: Unmasking Alignment Faking LLMs by Manipulating Their Internals" (Clymer et al., 2024), Section 3.3. 