AsymmetryZero Framework Operationalizes Expert Preferences as Semantic Evals
The AsymmetryZero framework, outlined in arXiv paper 2605.04083, tackles the issue of integrating subjective, procedural, and domain-specific preferences from human experts into reinforcement learning evaluation systems. Each task is framed as a stable evaluation contract that clarifies grading criteria, detailing what aspects are evaluated, the judgment process for each criterion, and how outcomes are determined. This contract can be utilized with Inspect for model-only assessments or the Harbor Framework for agent evaluations, facilitating comparable scores and shared audit artifacts in both contexts. The emphasis of this research is on evaluation design in reinforcement learning, particularly for real-world tasks with complex requirements that are challenging to encode as precise targets or open-ended preferences.
Key facts
- AsymmetryZero is a framework for operationalizing human expert preferences as semantic evals.
- It represents each task as a stable evaluation contract.
- The contract specifies grading criteria, judgment methods, and aggregation into a task outcome.
- It can be executed using Inspect for model-only evaluations.
- It can also be executed using the Harbor Framework for agentic evaluations.
- The framework enables comparable scores and shared audit artifacts across both settings.
- The work is published on arXiv with identifier 2605.04083.
- It addresses challenges in RL evaluation design for subjective and domain-specific tasks.
Entities
Institutions
- arXiv