Applied LLMs
TIES and DARE Merging
TIES and DARE are two parameter-space merging algorithms that resolve weight interference when combining multiple fine-tuned models into one, avoiding retraining entirely.
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Merging the weights of two fine-tuned language models with a simple average reliably degrades performance on both tasks. The culprit is not averaging itself; it is interference between the delta parameters each model has accumulated. TIES and DARE are two algorithmic responses to that interference, and together they underpin most of the serious model-merging work happening in 2023-2025.
The Task Vector Picture
To reason about merging, you need the concept of a task vector (introduced in Ilharco et al., ICLR 2023). Given a pre-trained model with weights \(\theta_0\) and a fine-tuned model with weights \(\theta_{ft}\), the task vector is simply:
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