  Although pre-trained language models encode generic knowledge beneficial for
planning and control, they may fail to generate appropriate control policies
for domain-specific tasks. Existing fine-tuning methods use human feedback to
address this limitation, however, sourcing human feedback is labor intensive
and costly. We present a fully automated approach to fine-tune pre-trained
language models for applications in autonomous systems, bridging the gap
between generic knowledge and domain-specific requirements while reducing cost.
The method synthesizes automaton-based controllers from pre-trained models
guided by natural language task descriptions. These controllers are verifiable
against independently provided specifications within a world model, which can
be abstract or obtained from a high-fidelity simulator. Controllers with high
compliance with the desired specifications receive higher ranks, guiding the
iterative fine-tuning process. We provide quantitative evidences, primarily in
autonomous driving, to demonstrate the method's effectiveness across multiple
tasks. The results indicate an improvement in percentage of specifications
satisfied by the controller from 60% to 90%.
