arXiv (All)
open access
DOHF: Online Diffusion Fine-tuning with Doob's $h$-transform Guidance
Reward-based diffusion fine-tuning faces practical challenges when desirable outcomes are rare or conditioning corrections are costly to estimate. In this work, we propose Diffusion Online $h$-guidance Fine-tuning (DOHF), which turns Doob's $h$-transform into a practical online training algorithm. D
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
- Designing for the Next Click: Bandits for Real-Time Page Layout#798473
- Designing for the Next Click: Bandits for Real-Time Page Layout#797060
- Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling#784757
- Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling#783167
- HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training#1118282
Record · ID 1115838
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