Existing approaches for LLMs in the chemical domain typically depend on costly pretraining with large unimodal datasets for reaction prediction and retrosynthesis , or task-specific fine-tuning for language-molecule learning and molecule editing . Other initiatives focus on multitask learning, which involves resource-intensive pretraining and large multitask datasets . By contrast, we investigate synergies between finetuning alignment and model merging to enhance molecule language models with minimal training.