LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget

Changhai Zhou, Kieran Liu, Yuhua Zhou, Qian Qiao, Jun Gao, Harry Zhang, Irvine Lu, Nolan Ho, Lucian Li, Andrew Lei, Cleon Cheng, Steven Chiang, Yihang Zeng, Di Zhang, Rio Yang, Kaijie Chen, Andrew Chen, Pony Ma, Weizhong Zhang, Cheng Jin

A widening gap separates million-token inference from RL post-training, which remains at 256K tokens or below. The gap matters for AI agents, whose observations, tool outputs, documents, and decisions accumulate over long trajectories. Unlike inference, GRPO scores and backpropagates through multiple responses conditioned on one history, making attention and long-lived backward state a primary GPU-memory barrier. We present LongStraw, an objective- and architecture-aware system for million-token RL post-training under a fixed GPU budget. Resident state retains only model-native prompt state needed by later tokens, not the full graph. Response replay restores that state, scores graph-free old/reference branches, rebuilds one policy response under autograd, backpropagates, and pops to the prompt boundary. Distributed, model-native execution assigns state and gradients to context and expert owners. State is shared within a group and recaptured or reused under a measured refresh policy after an update. For Qwen3.6-27B, LongStraw combines compact GDN state, CP8-sharded KV pages, exact attention composition, and reverse block replay. For GLM-5.2, it combines CPU-resident MLA/DSA state, IndexShare-aware selection, and native top-8 MoE replay over CP32/EP32. On eight H20 GPUs, Qwen completes exact-attention response-only GRPO at 2,097,152 positions for G=2 and G=8; a 4,456,448-position prefix supports eight G=8 cycles (64 replays) at 83.894 GB per rank. On 32 H20 GPUs, GLM completes deterministic 2M execution and two 78-layer backward passes; archived external integrations provide preliminary validation of the real vLLM-DAPO-Tinker/Megatron loop. Practical training context is set by resident-state lifetime, replay, and distributed ownership rather than attention kernels alone. The measured objective is response-only execution, not full-sequence gradient equivalence.