Core problem. Frontier reasoning systems can now swallow million-token contexts, but RL post-training is still stuck at 256K — pushing training context from inference to 2M+ is the real bottleneck for agentic RL. The paper observes that AI agents' observations, tool output, documents, and prior decisions accumulate along the trajectory; if post-training length can't go up, long-horizon agent capabilities can't come out. What LongStraw does. arXiv 2607.14952 (MindLab-Research, Changhai Zhou and 20 co-authors), posted 2026-07-16, gives an architecture-aware execution stack designed for million-token RL post-training. Three key moves: (1) shared prompt doesn't open autograd, inference-only evaluation prefix, keeping only the model state truly needed by subsequent tokens; (2) response short-branch replay, splitting long responses into short branches replayed segment by segment, compressing the "live training graph" to the minimum; (3) architecture-specific optimization — instead of writing a general solution, specialize for two real backbones: hybrid recurrent + full-attention Qwen3.6-27B, and compressed-attention MoE GLM-5.2. The algorithm side uses today's most popular GRPO. Measured data. On 8 H20s, Qwen3.6-27B's grouped scoring + response backward runs to 2.1M positions, with group size going from 2 to 8 only adding 0.21 GB of peak VRAM; extreme stress test reached 4.46M. On 32 H20s, GLM-5.2's all 78 layers of end-to-end forward were run through on 2.1M-token prompts — this carries more weight because compressed-attention MoE's inter-layer state is far more complex. Personal take. The key here isn't "finishing a complete RL step", but "daring to release this execution stack as-is". The authors explicitly mark "establishes execution capacity rather than complete training correctness, some distributed forward and gradient composition paths remain incomplete" — this restraint is too rare in RL infra papers, far more solid than the cliché talk of "another 1M context RL". The real bottleneck of agentic RL is right at context length; LongStraw turns "million-token post-training" from a slogan into executable engineering. Code is open-sourced at github.com/MindLab-Research/longstraw. The next step is who can use it to make a complete training flow that truly trains long-horizon agents.