AI BriefingDynaMiCS: Dynamic Mixture-Based LLM Fine-Tuning with Performance Constraints
Key point
DynaMiCS is a dynamic mixture optimization technique that helps boost performance in a specific domain while preserving core capabilities such as existing knowledge and safety.
Details
In the Multi-domain fine-tuning process for LLMs, it is very important to improve performance on the target domain while preventing degradation of existing capabilities such as common sense, instruction following, and safety. Existing data mixture strategies relied on fixed heuristics or adaptive rules, which limited their ability to explicitly preserve specific capabilities.
To address this, the proposed DynaMiCS formulates multi-domain fine-tuning as a Constrained Optimization Problem. This approach performs short Domain-specific probing at every update step to estimate a local Slope matrix.
This dynamic mixture optimization approach provides the following benefits.
- Performance preservation: Explicitly prevents performance degradation in constrained domains
- Dynamic optimization: Adjusts data mixture ratios in real time according to the training process, rather than using fixed ratios
- Efficient learning: Maintains the model's overall capabilities in a balanced way while focusing on the target domain
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