  Language model agents (LMA) recently emerged as a promising paradigm on
muti-step decision making tasks, often outperforming humans and other
reinforcement learning agents. Despite the promise, their performance on
real-world applications that often involve combinations of tasks is still
underexplored. In this work, we introduce a new benchmark, called CompWoB -- 50
new compositional web automation tasks reflecting more realistic assumptions.
We show that while existing prompted LMAs (gpt-3.5-turbo or gpt-4) achieve
94.0% average success rate on base tasks, their performance degrades to 24.9%
success rate on compositional tasks. On the other hand, transferred LMAs
(finetuned only on base tasks) show less generalization gap, dropping from
85.4% to 54.8%. By balancing data distribution across tasks, we train a new
model, HTML-T5++, that surpasses human-level performance (95.2%) on MiniWoB,
and achieves the best zero-shot performance on CompWoB (61.5%). While these
highlight the promise of small-scale finetuned and transferred models for task
compositionality, their performance further degrades under different
instruction compositions changing combinational order. In contrast to the
recent remarkable success of LMA, our benchmark and detailed analysis emphasize
the necessity of building LMAs that are robust and generalizable to task
compositionality for real-world deployment.
