Mixed Training Mitigates Catastrophic Forgetting in Mathematical Reasoning Finetuning

The study addresses a critical challenge in AI: preventing large language models from forgetting previously learned information during fine-tuning. The research likely proposes a novel mixed training approach to enhance the performance and stability of models in mathematical reasoning tasks.

• •Addresses the problem of catastrophic forgetting in LLMs.
• •Focuses on improving mathematical reasoning capabilities.
• •Suggests a mixed training methodology for better performance.