The authors fine-tune small language models on micro-datasets of Stoic philosophy texts (just 300 examples) using preference optimization methods (ORPO, AlphaPO) to see if models can internalize a nuanced philosophical framework under extreme data constraints. Evaluated by a multi-model critic, the fine-tuned models align well with inward-facing Stoic virtues (self-discipline, reflection) and approach few-shot prompting performance, but all models—including few-shot baselines—fail on outward-facing cosmopolitan duties, suggesting a representational limitation of small models.
Main takeaways:
- 300 high-quality Stoic text examples + preference optimization (ORPO, AlphaPO) can induce strong alignment with inward Stoic virtues
- Fine-tuned models nearly match few-shot prompting on inward virtues, freeing up context window
- All models (fine-tuned and few-shot) persistently fail on outward-facing cosmopolitan duties
- Limitation appears representational—small models lack capacity for certain philosophical nuances, not fixable by micro-dataset tuning alone