WebArbiter-7B

A principle-guided reasoning Process Reward Model for web agents

Published at ICLR 2026

Paper | Code | Website | Collection | Demo

Introduction

WebArbiter-7B is a 7B reasoning Process Reward Model (PRM) for web agents, built on Qwen2.5-7B-Instruct. Unlike scalar or checklist-based reward models, WebArbiter formulates step-level reward modeling as structured text generation — producing interpretable, principle-inducing justifications that conclude with a preference verdict identifying the action most conducive to task completion.

On WEBPRMBENCH, WebArbiter-7B achieves an Avg. BoN Acc of 74.60%, outperforming GPT-5 by 9.1 points and the previous SOTA WebPRM (WebShepherd-8B) by 31 points. In reward-guided trajectory search on WebArena-Lite, it surpasses WebShepherd-8B by up to 6.4 points in success rate.

Highlights

• Reasoning as reward: Generates structured <State>, <Criteria>, <Analysis>, and <Answer> outputs with auditable reasoning chains, instead of scalar scores or brittle checklists.
• Principle-inducing evaluation: Dynamically derives evaluation principles from user intent and page state, enabling robust assessment that generalizes across environments.
• Two-stage training: Reasoning distillation from o3 (SFT) followed by RL with Verifiable Rewards (GRPO) to correct teacher biases and align verdicts with ground-truth correctness.
• Robust generalization: SOTA performance across all four WebPRMBench environments, including out-of-domain enterprise workflows (WorkArena) and open-world websites (AssistantBench).

Results on WebPRMBench

Models marked with ⋆ are ours. Bold = best overall.

Reward-Guided Trajectory Search (WebArena-Lite)

WebArbiter also excels as a practical reward signal for trajectory search. Using Best-of-5 sampling with a Knockout Tournament mechanism on WebArena-Lite:

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZYao720/WebArbiter-7B"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# Construct your prompt following the WebPRMBench format.
# See https://huggingface.co/datasets/ZYao720/WEBPRMBENCH for examples.
user_prompt = "..."  # evaluation prompt with intent, AXTree, trajectory, two responses

messages = [{"role": "user", "content": user_prompt}]
input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt",
).to(model.device)

with torch.no_grad():
    output = model.generate(input_ids=input_ids, max_new_tokens=2048, do_sample=False)

response = tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True)
print(response)

Example output:

<State>The user is on the DuckDuckGo homepage with a search box visible.
Relevant AXTree elements: [1] textbox 'Search', [2] button 'Search'.</State>
<Criteria>1. Goal alignment (weight 0.6) — Does the action advance the search task?
2. Element reference accuracy (weight 0.25) — Is the referenced element correct?
3. Efficiency (weight 0.15) — Does the action avoid unnecessary steps?</Criteria>
<Analysis>Response 1 directly fills the search query into the textbox, which is the
most direct path to completing the search task. Response 2 clicks an irrelevant link
that does not contribute to the search goal.</Analysis>
<Answer>Response 1</Answer>

Training Details

Key training insights (from ablation studies in the paper):

• Explicit principles are essential — removing them notably degrades performance, especially on out-of-domain environments.
• Cold-start RL without reasoning distillation is unstable across environments.
• Reasoning distillation provides stable discrimination, while RL acts as an amplifier that widens the margin between correct and incorrect judgments.

Intended Uses

WebArbiter-7B is designed to:

• Evaluate web agent actions: Given a web state and two candidate actions, determine which better advances the user's task.
• Guide trajectory search: Serve as a reward signal for Best-of-N sampling or tree search during web agent execution.
• Provide interpretable feedback: Generate structured justifications explaining why one action is preferred, useful for debugging and analysis.

Limitations

• Text-only observations: WebArbiter relies on accessibility tree representations without visual observations. In environments where layout, spatial arrangement, or visual cues carry task-relevant information, this text-only formulation may miss critical signals.
• English-only: Training and evaluation are conducted exclusively in English-language web environments.
• Safe-action bias: The model may sometimes overvalue cautious actions (e.g., hover over click) because the accessibility tree does not encode interaction effects.
• Element reference hallucination: When a candidate action's reasoning is strongly task-aligned, the model may trust the semantic signal over low-level bid verification, potentially missing incorrect element references.

License

This model is released under Apache 2.0, following the base model Qwen2.5-7B-Instruct.

Related Resources

Citation

@misc{zhang2026ZYao720principleguidedreasoningprocess,
      title={WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents}, 
      author={Yao Zhang and Shijie Tang and Zeyu Li and Zhen Han and Volker Tresp},
      year={2026},
      eprint={2601.21872},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.21872}, 
}