AsymmetryZero: A Framework for Operationalizing Human Expert Preferences as Semantic Evals
Much of the focus in RL today is on evaluation design: building meaningful evals that serve simultaneously as benchmarks and as well-defined reward signals for post-training. Yet, many real-world tasks are governed by subjective, procedural, and domain-specific requirements that are difficult to encode as exact-match targets or open-ended preference judgments frequently used in RL pipelines today. In this work, we present AsymmetryZero, a framework for operationalizing human expert preferences as semantic evals. AsymmetryZero represents each task as a stable evaluation contract that makes grading criteria explicit: what is being graded, how each criterion is judged, and how criterion-leve...
| Authors | Tadhg Looram, Lucas Nuzzi, Kyle Waters et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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