AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories

• URL: http://arxiv.org/abs/2504.08942v1
• Date: Fri, 11 Apr 2025 19:49:22 GMT
• Title: AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
• Authors: Xing Han Lù, Amirhossein Kazemnejad, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy,
• Abstract summary: We propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents.<n>Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks.<n>We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents.
• Score: 59.214178488091584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents.<n>We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature.<n>We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
  This study enhances an LLM-based web agent by simply refining its observation and action space. AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
  arXiv  Detail & Related papers  (2024-10-17T17:50:38Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• AutoPenBench: Benchmarking Generative Agents for Penetration Testing [42.681170697805726]
  This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction.
  arXiv  Detail & Related papers  (2024-10-04T08:24:15Z)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena.<n>We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search.<n>We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)
• $τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains [43.43344028212623]
  $tau$-bench is a benchmark emulating dynamic conversations between a user and a language agent. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state.
  arXiv  Detail & Related papers  (2024-06-17T19:33:08Z)
• WebSuite: Systematically Evaluating Why Web Agents Fail [2.200477647229223]
  We describe WebSuite, the first diagnostic benchmark for generalist web agents. This benchmark suite consists of both individual tasks, such as clicking a button, and end-to-end tasks, such as adding an item to a cart. We evaluate two popular generalist web agents, one text-based and one multimodal, and identify unique weaknesses for each agent.
  arXiv  Detail & Related papers  (2024-06-01T00:32:26Z)
• Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions [77.66677127535222]
  Auto-Arena is an innovative framework that automates the entire evaluation process using LLM-powered agents. In our experiments, Auto-Arena shows a 92.14% correlation with human preferences, surpassing all previous expert-annotated benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T17:19:19Z)
• AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents [19.439775106707344]
  AgentQuest is a framework where benchmarks and metrics are modular and easily through well documented and easy-to-use APIs. We offer two new evaluation metrics that can reliably track LLM agent progress while solving a task. We exemplify the utility of the metrics on two use cases wherein we identify common failure points and refine the agent architecture to obtain a significant performance increase.
  arXiv  Detail & Related papers  (2024-04-09T16:01:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.