Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models

2026-07-28

AI Summary

This work addresses the long-standing isolation among research domains such as alignment training, model organisms, and toy models, which has hindered empirical cross-pollination and led to redundant exploration and inefficiency. For the first time, it systematically transfers supervised fine-tuning (SFT) practices across these domains by integrating cross-model output training, mixed-strategy data, and benign fine-tuning to rigorously evaluate the portability of key findings. The study demonstrates three successful transfer effects: enhanced behavioral generalization, mitigation of capability degradation, and the critical insight that preserving capabilities alone is insufficient to ensure robustness in subsequent training phases. These results underscore both the efficacy and limitations of reusing methodologies across domains, thereby fostering more synergistic development across disparate research areas.

Abstract

Alignment training, model organisms, and toy models are usually treated as separate research areas. But projects in all three frequently use supervised fine-tuning (SFT) to pursue the same underlying goals. When projects share a goal, we should test whether lessons learned from one area transfer to the other areas. We study three such transfers, each taking a lesson developed in one SFT setting and testing it in another. First, we port a lesson about behavior generalization from alignment training into toy models. Training on the reason for a behavior, as in Teaching Claude Why, can make the behavior generalize better than training on examples of the behavior alone. Second, we port a lesson about capability preservation from model organisms into the Model-Spec Midtraining alignment setting. SFT on outputs written by a model other than the student (off-model outputs) can damage capabilities when trained on. Mixing in benign on-model (and on-policy) data into our training can prevent most of this damage while still embedding the target behavior. Third, we port a lesson about robustness from model organisms into the same alignment setting. We find that follow-up benign SFT can erase the alignment behavior while preserving capabilities, showing that capability preservation alone does not ensure robustness to subsequent training. Our work illustrates how porting SFT lessons between different research fields can uplift them all, suggesting more researchers should borrow techniques from outside their own areas.

Problem

• supervised fine-tuning
• alignment training
• model organisms
• toy models
• lesson transfer

Innovation

• supervised fine-tuning
• cross-domain transfer
• behavior generalization
• capability preservation
• alignment robustness