arXiv (All)
open access
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Reinforcement Learning-based post-training of Large Language Models (LLM) has been successfully applied to improve their reasoning capabilities. Existing pipelines primarily finetune LLMs on a fixed pool of problems specified prior to training using the GRPO loss. This is fundamentally limiting, as
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
- RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner#1037681
- Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model#924065
- Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model#919836
- To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models#984116
- ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning#793014
Record · ID 1130014
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