arXiv (All)
open access
Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL
ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in ELBO-based RL has large impact on performance, and it also provides a unified view (as we show in this work) to understand prediction losses heuristica
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
- On the Equality of the ELBO to a Sum of Entropies at Stationary Points of Learning#925693
- CoupVisor: Strategy Optimization by Round and Challenge Decision Support#616999
- CoupVisor: Strategy Optimization by Round and Challenge Decision Support#616499
- Diversified Multinomial Logit Contextual Bandits#1118649
- Balanced scorecard#246229
Record · ID 1116996
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