  In this report, we explore the ability of language model agents to acquire
resources, create copies of themselves, and adapt to novel challenges they
encounter in the wild. We refer to this cluster of capabilities as "autonomous
replication and adaptation" or ARA. We believe that systems capable of ARA
could have wide-reaching and hard-to-anticipate consequences, and that
measuring and forecasting ARA may be useful for informing measures around
security, monitoring, and alignment. Additionally, once a system is capable of
ARA, placing bounds on a system's capabilities may become significantly more
difficult.
  We construct four simple example agents that combine language models with
tools that allow them to take actions in the world. We then evaluate these
agents on 12 tasks relevant to ARA. We find that these language model agents
can only complete the easiest tasks from this list, although they make some
progress on the more challenging tasks. Unfortunately, these evaluations are
not adequate to rule out the possibility that near-future agents will be
capable of ARA. In particular, we do not think that these evaluations provide
good assurance that the ``next generation'' of language models (e.g. 100x
effective compute scaleup on existing models) will not yield agents capable of
ARA, unless intermediate evaluations are performed during pretraining.
Relatedly, we expect that fine-tuning of the existing models could produce
substantially more competent agents, even if the fine-tuning is not directly
targeted at ARA.
