SteP: Stacked LLM Policies for Web Actions
Performing tasks on the web presents fundamental challenges to large language
models (LLMs), including combinatorially large open-world tasks and variations
across web interfaces. Simply specifying a large prompt to handle all possible
behaviors and states is extremely complex, and results in behavior leaks
between unrelated behaviors. Decomposition to distinct policies can address
this challenge, but requires carefully handing off control between policies. We
propose Stacked LLM Policies for Web Actions (SteP), an approach to dynamically
compose policies to solve a diverse set of web tasks. SteP defines a Markov
Decision Process where the state is a stack of policies representing the
control state, i.e., the chain of policy calls. Unlike traditional methods that
are restricted to static hierarchies, SteP enables dynamic control that adapts
to the complexity of the task. We evaluate SteP against multiple baselines and
web environments including WebArena, MiniWoB++, and a CRM. On WebArena, SteP
improves (14.9\% to 33.5\%) over SOTA that use GPT-4 policies, while on
MiniWob++, SteP is competitive with prior works while using significantly less
data. Our code and data are available at
https://asappresearch.github.io/webagents-step.
651f6808e17ef6a94e063719
Paloma Sodhi
extracted_pending
2023-10-06T01:51:08.453Z
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Paloma Sodhi
user
psodhi-asapp
651f6808e17ef6a94e06371a
S. R. K. Branavan
extracted_pending
2024-01-31T19:30:07.860Z
65ba9f9b7677ff48f59e8c3c
/avatars/22b8e6a9a341ab83b6977d80a9822d34.svg
S.R.K. Branavan
user
branavan-asapp
651f6808e17ef6a94e06371b
Ryan McDonald
SteP: Stacked LLM Policies for Web Actions
Performing tasks on the web presents fundamental challenges to large language
models (LLMs), including combinatorially large open-world tasks and variations
across web interfaces. Simply specifying a large prompt to handle all possible
behaviors and states is extremely complex, and results in behavior leaks
between unrelated behaviors. Decomposition to distinct policies can address
this challenge, but requires carefully handing off control between policies. We
propose Stacked LLM Policies for Web Actions (SteP), an approach to dynamically
compose policies to solve a diverse set of web tasks. SteP defines a Markov
Decision Process where the state is a stack of policies representing the
control state, i.e., the chain of policy calls. Unlike traditional methods that
are restricted to static hierarchies, SteP enables dynamic control that adapts
to the complexity of the task. We evaluate SteP against multiple baselines and
web environments including WebArena, MiniWoB++, and a CRM. On WebArena, SteP
improves (14.9\% to 33.5\%) over SOTA that use GPT-4 policies, while on
MiniWob++, SteP is competitive with prior works while using significantly less
data. Our code and data are available at
https://asappresearch.github.io/webagents-step.
Stacked LLM Policies for Web Actions dynamically compose policies to handle diverse web tasks, improving performance over baseline methods.
large language models LLMs web interfaces policies behavior leaks Markov Decision Process control state dynamic control WebArena MiniWoB++ CRM GPT-4 policies