LongStraw 7/17 (arXiv 2607.14952, #1 Daily Paper): million-token RL execution stack, 2.1M positions on 8x H20 + 4.46M stress test

arXiv 2607.14952 (indexed 7/17, 207 upvotes, #1 Daily Paper, Mind Lab) introduces LongStraw, an architecture-aware execution stack for million-token RL post-training under a fixed GPU budget, instantiated with GRPO. Methodology: evaluates shared prompt once without autograd; retains only model-specific state needed by later tokens; replays short response branches one at a time under autograd; reduces live training graph from full prompt+response to a single response branch; trades replay time for lower GPU memory. Model implementations: Qwen3.6-27B (hybrid recurrent + full-attention) and GLM-5.2 (compressed-attention MoE, 78 layers). Reported benchmark numbers: on 8x H20 GPUs completes grouped Qwen scoring and response backward at 2.1M positions for groups of 2 and 8; group size scaling cost is only +0.21 GB peak allocated memory per group increase; stress test reaches 4.46M positions; on 32x H20 GPUs end-to-end validation for 2.1M-token prompt across all 78 layers of GLM-5.2. Key insights: targets the gap between inference (~1M token contexts) and RL post-training (often ≤256K tokens); especially relevant for AI agents with accumulating trajectories (observations, tool outputs, prior decisions). Authors note these experiments establish execution capacity, not complete training correctness — captured prompt state is detached and some distributed forward / gradient composition paths remain incomplete.