Part of an abstract from referenced articles: > Even though we are able to increase accuracy on MATH, our results show that accuracy remains relatively low, ev

Part of an abstract from referenced articles: > Even though we are able to increase accuracy on MATH, our results show that accuracy remains relatively low, even with enormous Transformer models. Moreover, we find that simply increasing budgets and model parameter counts will be impractical for achieving strong mathematical reasoning if scaling trends continue. While scaling Transformers is automatically solving most other text-based tasks, scaling is not currently solving MATH. To have more traction on mathematical problem solving we will likely need new algorithmic advancements from the broader research community.[1] 1: https://arxiv.org/abs/2103.03874 https://arxiv.org/abs/2103.03874