arXiv (All)
open access
Rollout-Level Advantage-Prioritized Experience Replay for GRPO
Reinforcement learning from verifiable rewards with GRPO is a standard approach for post-training reasoning LLMs. It remains sample inefficient. Each rollout is used for a single gradient update and then discarded. Naive replay is not well suited in this setting because LLM policies drift quickly pe
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
- Group Adaptive Clipping Policy Optimization#819892
- Group Adaptive Clipping Policy Optimization#809896
- Reinforcement Learning for LLM Post-Training: A Survey#189201
- GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation#1035836
- Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model#924065
Record · ID 985117
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