Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training
arXiv:2606.26102v1 Announce Type: cross Abstract: Standard post-training pipelines apply supervised fine-tuning (SFT) and reinforcement learning (RL) to make language models helpful, but these processes may inadvertently degrade values instilled during pre-training. We investigate whether the domain of post-training data differentially affects the retention of animal compassion values in a Llama 3.1 8B model mid-trained on compassion-oriented synthetic data, using both SFT (helpfulness via Dolly-15k vs. coding via Magicoder-110K) and GRPO (helpfulness via RLHFlow vs. coding via Magicoder), eva
The rapid advancement and deployment of large language models necessitate a deeper understanding of how post-training influences their ethical alignment and value retention.
Ensuring AI systems maintain intended values, particularly 'compassion,' is critical for their safe and beneficial integration into society, impacting trust and regulatory frameworks.
This research highlights the complex trade-offs between making AI 'helpful' and preserving core values during post-training, suggesting that alignment processes can unintentionally degrade ethical principles.
- · AI ethics researchers
- · Developers of custom alignment techniques
- · Organizations prioritizing value-aligned AI
- · Developers using off-the-shelf SFT/RL pipelines without validation
- · Users relying solely on 'helpful' AI without understanding value degradation
- · Companies neglecting pre-training and mid-training ethical considerations
AI developers will need to re-evaluate and potentially redesign their post-training pipelines to better preserve pre-trained values.
Increased demand for tools and methodologies to quantitatively measure and prevent value degradation during fine-tuning processes.
New certification standards or regulatory scrutiny may emerge for 'value-aligned' AI models, requiring transparent methodologies for demonstrating ethical retention.
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Read at arXiv cs.AI