EvoMerge: Neuroevolution for Large Language Models
Yushu Jiang
Extensive fine-tuning on Large Language Models does not always yield better
results. Oftentimes, models tend to get better at imitating one form of data
without gaining greater reasoning ability and may even end up losing some
intelligence. Here I introduce EvoMerge, a systematic approach to large
language model training and merging. Leveraging model merging for weight
crossover and fine-tuning for weight mutation, EvoMerge establishes an
evolutionary process aimed at pushing models beyond the limits of conventional
fine-tuning.