arXiv (OAI Expanded)
open access
R3: Robust Rubric-Agnostic Reward Models
Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models are typically optimized for narrow objectives, limiting their generalizability to broader downstream tasks. Moreover, the
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
- From Constitutions to Control: Interpretable Rewards for Aligning Language Models#1117798
- 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
- Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agents#1115951
- UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms#973528
Record · ID 1110091
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