KOASAS: KAIST Open Access Self-Archiving System
metadata only
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback
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
- RPS: Information Elicitation with Reinforcement Prompt Selection#14045
- PromptEcho: Annotation-Free Reward from Vision-Language Models for Text-to-Image Reinforcement Learning#13156
- WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps#1063244
- GMTS: Gradient Magnitude-based Token Selection Improves RLVR Training for LLM Reasoning#808699
- Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View#609600
Record · ID 436429
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