AI in Chemistry is essential for developing scalable and cost-effective scientific solutions, such as pioneering drugs , advanced materials , and improved chemical processes . The vast search spaces in which these solutions reside make chemical language models crucial for accelerating scientific discovery (AI4Science and Quantum, 2023; . Recent trends have led to the use of multimodal models to learn molecular and linguistic representations, either in separate but coordinated spaces , in a common space , or through dual approaches . These models often rely heavily on extensive supervised fine-tuning. However, merely increasing model size and data does not guarantee improvement . Thus we propose focusing on novel training methods to improve molecule language models.