Abstract: In nature, animals with exceptional locomotion abilities, such as cougars, often possess asymmetric fore and hind legs, with their powerful hind legs acting as reservoirs of energy for leaps. Inspired by this biological concept, a co-optimization method for mechanical structures and control strategies is proposed, focusing on optimizing the leg length of the robot to enhance its overall motion performance. Firstly, a novel pretraining-finetuning framework is introduced, which not only guarantees optimal control strategies for each mechanical candidate but also improves the training efficiency of the algorithm. Additionally, spatial domain randomization is integrated with discount regularization, which remarkably improves the generalization ability of the pretraining network. Experimental results indicate that the proposed pretraining-finetuning framework significantly enhances the overall co-design performance with less time consumption. Moreover, the co-design strategy substantially exceeds the conventional method of independently optimizing control strategies, providing an innovative approach to enhancing the extreme parkour capabilities of quadruped robots.