arXiv (All)
open access
Splitting Prompt Prefill from Response Replay for Context-Parallel Long-Context LLM Post-Training
Training long-context LLM policies with RL requires re-evaluating groups of sampled responses under the updated policy, an update-stage attention workload that differs sharply from pre-training: each group shares one long prompt that fans out into multiple response branches. Standard context paralle
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Splitting Prompt Prefill from Response Replay for Context-Parallel Long-Context LLM Post-Training#1108735
- Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool#1014774
- Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool#405615
- Language Models Can Control Their Own Attention#924873
- Language Models Can Control Their Own Attention#920639
Record · ID 1117845
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive