AgentRewardBench: Evaluating Automatic Evaluations of Web Agent
  Trajectories
Web agents enable users to perform tasks on web browsers through natural
language interaction. Evaluating web agents trajectories is an important
problem, since it helps us determine whether the agent successfully completed
the tasks. Rule-based methods are widely used for this purpose, but they are
challenging to extend to new tasks and may not always recognize successful
trajectories. We may achieve higher accuracy through human evaluation, but the
process would be substantially slower and more expensive. Automatic evaluations
with LLMs may avoid the challenges of designing new rules and manually
annotating trajectories, enabling faster and cost-effective evaluation.
However, it is unclear how effective they are at evaluating web agents. To this
end, we propose AgentRewardBench, the first benchmark to assess the
effectiveness of LLM judges for evaluating web agents. AgentRewardBench
contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in
AgentRewardBench is reviewed by an expert, who answers questions pertaining to
the success, side effects, and repetitiveness of the agent. Using our
benchmark, we evaluate 12 LLM judges and find that no single LLM excels across
all benchmarks. We also find that the rule-based evaluation used by common
benchmarks tends to underreport the success rate of web agents, highlighting a
key weakness of rule-based evaluation and the need to develop more flexible
automatic evaluations. We release the benchmark at:
https://agent-reward-bench.github.io
67fdadafdc27362617bbe714
Xing Han Lù
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2025-04-15T07:55:07.746Z
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Xing Han Lù
user
xhluca
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Amirhossein Kazemnejad
admin_assigned
2025-04-15T08:22:50.671Z
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Amirhossein Kazemnejad
user
kazemnejad
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Nicholas Meade
admin_assigned
2025-04-15T08:22:56.742Z
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Nicholas Meade
user
ncmeade
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Arkil Patel
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2025-04-15T07:55:05.412Z
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Arkil Patel
user
arkilpatel
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Dongchan Shin
admin_assigned
2025-04-15T08:23:03.255Z
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Dong Chan Shin
user
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Alejandra Zambrano
admin_assigned
2025-04-15T08:23:09.097Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/nxxfWGP_K0hf7BG1oM7c0.png
Alejandra Zambrano
user
alzambranolu
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Karolina Stańczak
admin_assigned
2025-04-15T08:23:14.768Z
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Karolina Stanczak
user
Karolina
67fdadafdc27362617bbe71b
Peter Shaw
admin_assigned
2025-04-15T08:23:22.550Z
631cf223fe95faea33561d5f
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Peter Shaw
user
PeterShaw
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Christopher J. Pal
67fdadafdc27362617bbe71d
Siva Reddy
extracted_pending
2025-04-15T00:51:59.987Z
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/avatars/6b250b58710a3287b85e4733c1824558.svg
Siva Reddy
user
sivareddyg
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent
  Trajectories
Web agents enable users to perform tasks on web browsers through natural
language interaction. Evaluating web agents trajectories is an important
problem, since it helps us determine whether the agent successfully completed
the tasks. Rule-based methods are widely used for this purpose, but they are
challenging to extend to new tasks and may not always recognize successful
trajectories. We may achieve higher accuracy through human evaluation, but the
process would be substantially slower and more expensive. Automatic evaluations
with LLMs may avoid the challenges of designing new rules and manually
annotating trajectories, enabling faster and cost-effective evaluation.
However, it is unclear how effective they are at evaluating web agents. To this
end, we propose AgentRewardBench, the first benchmark to assess the
effectiveness of LLM judges for evaluating web agents. AgentRewardBench
contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in
AgentRewardBench is reviewed by an expert, who answers questions pertaining to
the success, side effects, and repetitiveness of the agent. Using our
benchmark, we evaluate 12 LLM judges and find that no single LLM excels across
all benchmarks. We also find that the rule-based evaluation used by common
benchmarks tends to underreport the success rate of web agents, highlighting a
key weakness of rule-based evaluation and the need to develop more flexible
automatic evaluations. We release the benchmark at:
https://agent-reward-bench.github.io
AgentRewardBench is a benchmark evaluating the effectiveness of LLMs in assessing web agent performance, showing that rule-based methods may underreport success.
LLMs AgentRewardBench machine learning models natural language interaction web agents trajectory evaluation rule-based methods expert review