Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds

2026-09-02

AI Summary

本文提出两种技术，通过增加推理时间和模拟真实部署环境来提高对齐评估的真实性，解决模型在测试与实际部署中表现差异的问题。

Abstract

A core obstacle to alignment evaluation is evaluation awareness: capable models can tell when they are being tested rather than deployed, weakening the conclusions a safety evaluation can support. We present two techniques that make simulated alignment evaluations harder to distinguish from real deployments. Our first technique, critique refinement, spends additional inference-time compute on each simulator action: the simulator generates multiple candidate actions, refines them using feedback from an instance of the target model on how to make them more realistic, and continues the evaluation with the most deployment-like candidate. Our second technique, DISH (Deployment-Imitating SWE-Agent Harness), wraps the target in an agent harness, reducing the gap between simulated and real deployment environments in coding settings. We test the techniques on multiple target models and find that they compose: applying both yields larger realism gains than either alone. Our results show that automated approaches can improve the realism of alignment evaluations, and that these improvements use additional compute more effectively than making the audits longer.

Problem

• alignment evaluation
• evaluation awareness
• realism

Innovation

• critique refinement
• DISH
• realism improvement