Researchers have introduced OAPL, a new reinforcement learning algorithm designed to improve how Large Language Models (LLMs) learn complex reasoning for math and coding. Traditional methods often struggle when the training policy and the inference engine are out of sync, a common issue in large-scale, asynchronous computing. Instead of trying to force these mismatched systems to align, OAPL embraces this discrepancy by using a squared regression objective that functions effectively even with significant policy lag. This approach eliminates the need for complex importance sampling or heuristics that can destabilize training. Empirical results show that OAPL outperforms existing methods like GRPO on competitive benchmarks while using significantly fewer computational resources. Furthermore, the model maintains higher sequence entropy, which prevents the performance collapse often seen in other post-training techniques.