LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget
A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose observations, tool outputs, documents, and prior decisions accumulate over long trajectories. LongStraw is an architecture-aware execution stack for million-token RL post-training under a fixed GPU budget, instantiated with Group Relative Policy Optimization (GRPO). It evaluates the shared prompt wi
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- PossiblePossibly related (embedding) · 54%I benchmarked 13 models at 65K-128K context to find out what actually matters for agentic workloads →
- PossiblePossibly related (embedding) · 52%Show HN: ContextVault – Shared memory layer for your AI and your team →
- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- LinkedLinked via arxiv author · 85%Changhai Zhou →
“LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget”