LongStraw introduces an architecture-aware execution stack designed to enable million-token reinforcement learning post-training within a fixed GPU budget, addressing the growing gap between inference context lengths and RL post-training workloads. This approach optimizes resource utilization while supporting long-context environments critical for AI agents processing extended interaction histories. The framework aims to bridge the efficiency-performance divide by leveraging context-aware execution strategies. Read original