More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment
Abstract
Aligning large language models (LLMs) with human values is an increasingly critical step in post-training. Direct Preference Optimization (DPO) has emerged as a simple, yet effective alternative to reinforcement learning from human feedback (RLHF). Synthetic preference data with its low cost and high quality enable effective alignment through single- or multi-model generated preference data. Our study reveals a striking, safety-specific phenomenon associated with DPO alignment: Although multi-model generated data enhances performance on general tasks (ARC, Hellaswag, MMLU, TruthfulQA, Winogrande) by providing diverse responses, it also tends to facilitate reward hacking during training. This can lead to a high attack success rate (ASR) when models encounter jailbreaking prompts. The issue is particularly pronounced when employing stronger models like GPT-4o or larger models in the same family to generate chosen responses paired with target model self-generated rejected responses, resulting in dramatically poorer safety outcomes. Furthermore, with respect to safety, using solely self-generated responses (single-model generation) for both chosen and rejected pairs significantly outperforms configurations that incorporate responses from stronger models, whether used directly as chosen data or as part of a multi-model response pool. We demonstrate that multi-model preference data exhibits high linear separability between chosen and rejected responses, which allows models to exploit superficial cues rather than internalizing robust safety constraints. Our experiments, conducted on models from the Llama, Mistral, and Qwen families, consistently validate these findings.
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Key Insights: When More Data Hurts LLM Safety Alignment
Recent advances in aligning large language models (LLMs) with human values have leveraged Direct Preference Optimization (DPO) as a simpler alternative to RLHF. While using synthetic preference data from multiple models can boost general task performance, this study uncovers a critical safety pitfall: multi-model generated data can actually make models more vulnerable to jailbreaking attacks.
Figure: Attack Success Rate (ASR) for different data creation strategies. Self-generated data (green) yields the safest models.
Main Findings
- Self-generated preference data (single-model) leads to the safest LLMs, outperforming multi-model or strong-model generated data for safety alignment.
- Multi-model data (including responses from stronger models like GPT-4o) increases the risk of reward hacking, where models exploit superficial cues instead of learning robust safety constraints.
- General task performance remains similar across all data creation strategies, but safety outcomes diverge sharply.
- Linear separability: Multi-model data makes it too easy for models to distinguish between chosen and rejected responses, encouraging shortcut learning rather than true safety.
Why Does This Happen?
- Distributional mismatch: Mixing responses from different models introduces a gap between chosen and rejected responses, making it easier for models to exploit stylistic or irrelevant features.
- Reward hacking: Models trained on multi-model data rapidly minimize training loss but fail to generalize safety, as shown by high attack success rates.
Practical Implications
- For safety-critical LLM alignment, using the model’s own outputs (filtered by a reward model) is best.
- Relying on external or stronger model responses can degrade safety, even if general capabilities improve.
Figure: Training loss and data separability. Rapid loss drop (red) signals reward hacking, not true safety.
Conclusion
This work highlights a counterintuitive but crucial lesson: more diverse synthetic data is not always better for safety. For robust safety alignment, LLMs should learn from their own outputs, not from a mix of external model responses.
Read the full paper: arXiv:2504.02193
Junyuan "Jason" Hong
Postdoctoral Fellow
My research interest lies in the interaction of human-centered AI and healthcare.