arXiv (All)
open access
RewardHarness: Self-Evolving Agentic Post-Training
artificial-intelligencecomputation-and-languagecomputer-vision-and-pattern-recognitionmachine-learning
Aligning generative models with human preferences remains a central challenge. While RLHF is widely adopted, it relies on large-scale preference data. This limitation is particularly evident in evaluating instruction-guided image edits, even though humans can often learn such preferences from only a
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
- Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling#1202061
- Same Reward, Different Skills: When Multimodal RL Learns to Look#1157179
- LSR-Ben: A Logical and Scientific Reasoning Benchmark for Evaluating Process Reward Models#1143744
- Video Understanding Reward Modeling: A Robust Benchmark and Performant Reward Models#1143752
- Diffusion Reward Models#1135351
Record · ID 1327383
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