Semantic Scholar
metadata only
LLM Post-Training: A Deep Dive Into Reasoning Large Language Models
Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the research community is now increasingly shifting focus toward post-training techniques
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
- Beyond Corrected Actions: Training LLM Agents on Textual Critiques#1345190
- Relation-Aware Graph Foundation Model#1129079
- DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models#1143718
- Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions#1342678
- HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning#1187717
Record · ID 1274281
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