arXiv (All)
open access
Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on uniform data sampling, which ignores the semantic structure of the training data and the evolving capability of the training
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
- Libra: Efficient Resource Management for Agentic RL Post-Training#1011120
- A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes#1327942
- Efficient Resource-Shiftable RL Post-training with Generative Rewards#1341748
- Efficient Resource-Shiftable RL Post-training with Generative Rewards#1318979
- Sharpening Tax in Post-Training#1167700
Record · ID 1356093
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