Researchers have developed a new approach to optimizing the performance of large language models (LLMs) called Task-CoEvolve. The method involves selecting and adapting validation tasks as the model's harness evolves, rather than using a fixed set of tasks at every iteration. This allows for substantial performance gains without updating the underlying model weights. Experiments show that Task-CoEvolve outperforms existing approaches and reduces evaluation costs by 80%. The authors claim this can lead to significant improvements in LLM agent performance. --- Why it matters: This matters because optimizing large language models is a crucial step in developing more efficient and effective AI systems. By reducing the number of evaluations needed during optimization, Task-CoEvolve has the potential to speed up model development and deployment, which can be particularly important for applications where time-to-market is critical. Source: https://arxiv.org/abs/2608.20169