检测语言模型代理随时间偏离原始目标的现象，发现所有模型均有不同程度漂移。
Technical Report: Evaluating Goal Drift in Language Model Agents
- 通过环境压力引入竞争目标，模拟长期自主运行中的目标偏离。
- 最优模型在最严苛设置下维持超10万词符的目标一致性，但仍出现轻微漂移。
- 目标漂移与上下文增长导致的模式匹配倾向相关，适合关注安全性的研究者参考。
随着语言模型（LM）越来越多地作为自主代理部署，其对人类设定目标的稳定遵循成为确保安全运行的关键。当这些代理在缺乏人类监督的情况下长期独立运行时，即使初始目标设定清晰，也可能逐渐发生偏移。检测和度量目标漂移——即代理随时间偏离原始目标的倾向——面临巨大挑战，因为目标变化往往渐进，仅引发细微行为差异。本文提出一种新方法分析LM代理中的目标漂移。实验中，代理首先通过系统提示明确获得目标，随后在环境压力下遭遇竞争目标。我们发现，在最严苛评估场景下，表现最佳的代理（基于Claude 3.5 Sonnet的分层版本）能保持超过10万个令牌的目标一致性，但所有测试模型均表现出一定程度的目标漂移。此外，目标漂移与模型在上下文长度增加时愈发依赖模式匹配的行为趋势显著相关。
原文摘要 · Abstract (English)
As language models (LMs) are increasingly deployed as autonomous agents, their robust adherence to human-assigned objectives becomes crucial for safe operation. When these agents operate independently for extended periods without human oversight, even initially well-specified goals may gradually shift. Detecting and measuring goal drift - an agent's tendency to deviate from its original objective over time - presents significant challenges, as goals can shift gradually, causing only subtle behavioral changes. This paper proposes a novel approach to analyzing goal drift in LM agents. In our experiments, agents are first explicitly given a goal through their system prompt, then exposed to competing objectives through environmental pressures. We demonstrate that while the best-performing agent (a scaffolded version of Claude 3.5 Sonnet) maintains nearly perfect goal adherence for more than 100,000 tokens in our most difficult evaluation setting, all evaluated models exhibit some degree of goal drift. We also find that goal drift correlates with models' increasing susceptibility to pattern-matching behaviors as the context length grows.
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