A recent study published on arXiv demonstrates that training large language models on shorter reasoning tasks can enhance training efficiency without compromising their reasoning capabilities. The approach involves using easy samples as length regularizers, which helps prevent excessive verbosity during training. By focusing on these shorter, simpler tasks, the models maintain their ability to reason effectively while reducing the computational resources required. This method addresses the challenge of balancing task length and model performance, showing that shorter reasoning sequences can be as effective as longer ones. The findings suggest a promising direction for optimizing reinforcement learning in language models, aligning with ongoing research efforts to improve efficiency in AI training. Overall, the study supports the idea that frugal reasoning strategies can yield significant benefits in model development.