# Overview
Fork of “Selective Generalisation: Benchmarking Fine-Tuning Strategies to Control Misaligned Generalisation” (Azarbal, Clarke, Cocola, Factor, Cloud).

- Adds suffix support for user prompts in `projects/gemma_gcd`.
- Trains on assistant responses only (masks the user prompt).
- Includes larger dataset generation.

# Setup:
Follow the setup instructions in https://github.com/arianaazarbal/selective-generalization

# Run
1. `cd projects`
2. Edit `experiments/gemma_gcd_usrans1000_responly_suffsweep/attributes_to_vary.json` to select experiments.
3. Run:
```bash
uv run --env-file ../.env python attribute_sweep_multi_seed_run.py \
  gemma_gcd_usrans1000_responly_suffsweep \
  --experiment_script gemma_gcd/main.py \
  --dont_overwrite \
  --seeds 2 3 4 5 6 \
  --multi_seed_script multi_seed_run.py
```
