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Model: ZYao720/WebArbiter-7B Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- web-agent
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- process-reward-model
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- preference
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- reward-model
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- web-navigation
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- reasoning
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- grpo
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base_model: Qwen/Qwen2.5-7B-Instruct
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datasets:
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- ZYao720/WebArbiter-Data
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model-index:
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- name: WebArbiter-7B
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results:
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- task:
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type: text-generation
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name: Web Process Reward Modeling
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dataset:
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name: WebPRMBench
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type: ZYao720/WEBPRMBENCH
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metrics:
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- name: Avg Pairwise Accuracy
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type: accuracy
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value: 89.19
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- name: Avg BoN Accuracy
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type: accuracy
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value: 74.60
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---
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<div align="center">
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# WebArbiter-7B
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**A principle-guided reasoning Process Reward Model for web agents**
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**Published at ICLR 2026**
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[Paper](https://arxiv.org/abs/2601.21872) | [Code](https://github.com/YaoZhang720/WebArbiter) | [Website](https://yaozhang.ai/WebArbiter/) | [Collection](https://huggingface.co/collections/ZYao720/ZYao720-69cd5263871b22e11d90f80f) | [Demo](https://yaozhang.ai/WebArbiter/demo.html)
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</div>
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## Introduction
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**WebArbiter-7B** is a 7B reasoning Process Reward Model (PRM) for web agents, built on [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/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.
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On [WEBPRMBENCH](https://huggingface.co/datasets/ZYao720/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.
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## Highlights
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- **Reasoning as reward**: Generates structured `<State>`, `<Criteria>`, `<Analysis>`, and `<Answer>` outputs with auditable reasoning chains, instead of scalar scores or brittle checklists.
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- **Principle-inducing evaluation**: Dynamically derives evaluation principles from user intent and page state, enabling robust assessment that generalizes across environments.
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- **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.
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- **Robust generalization**: SOTA performance across all four WebPRMBench environments, including out-of-domain enterprise workflows (WorkArena) and open-world websites (AssistantBench).
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## Results on WebPRMBench
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Models marked with ⋆ are ours. **Bold** = best overall.
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| Model | Mind2Web | | WebArena | | AssistantBench | | WorkArena | | Avg. | |
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|-------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| | Pair | BoN | Pair | BoN | Pair | BoN | Pair | BoN | Pair | BoN |
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| *Proprietary LLM-as-judge* | | | | | | | | | | |
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| GPT-4o-mini | 81.74 | 50.92 | 78.23 | 56.72 | 89.17 | 73.33 | 81.43 | 46.70 | 82.64 | 56.92 |
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| GPT-4o | 79.99 | 52.62 | 84.58 | 66.67 | 85.83 | 66.67 | 84.33 | 55.19 | 83.68 | 60.29 |
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| GPT-5 | 80.86 | 62.39 | 84.83 | 71.64 | 81.67 | 63.33 | 81.14 | 64.62 | 82.13 | 65.50 |
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| Claude-3.7-Sonnet | 80.20 | 57.90 | 82.80 | 64.10 | 81.50 | 61.30 | 82.10 | 60.60 | 81.65 | 60.98 |
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| Gemini-2.5-Flash | 81.30 | 57.01 | 82.71 | 62.19 | 80.00 | 63.33 | 83.30 | 56.13 | 81.83 | 59.67 |
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| DeepSeek-R1 | 81.62 | 57.37 | 82.04 | 60.21 | 78.49 | 56.18 | 84.12 | 63.89 | 81.57 | 59.41 |
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| *Open-source LLM-as-judge* | | | | | | | | | | |
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| Qwen2.5-7B-Instruct | 77.79 | 39.18 | 74.88 | 42.79 | 84.17 | 53.33 | 77.58 | 35.85 | 77.61 | 42.78 |
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| Llama-3-70B-Instruct | 80.55 | 49.36 | 77.36 | 50.75 | 85.83 | 70.00 | 79.08 | 40.09 | 80.71 | 52.55 |
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| *WebPRMs* | | | | | | | | | | |
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| WebShepherd-8B | 86.66 | 73.69 | 68.33 | 43.88 | 55.92 | 30.00 | 54.56 | 25.53 | 64.34 | 43.28 |
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| ⋆ **WebArbiter-7B** | **97.07** | **89.53** | **88.43** | **68.66** | **89.17** | **70.00** | **82.09** | **70.19** | **89.19** | **74.60** |
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## Reward-Guided Trajectory Search (WebArena-Lite)
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WebArbiter also excels as a practical reward signal for trajectory search. Using Best-of-5 sampling with a Knockout Tournament mechanism on [WebArena-Lite](https://arxiv.org/abs/2408.06327):
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| Policy | WebPRM | Shopping | CMS | Reddit | GitLab | MAP | Avg. | Δ |
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|--------|--------|:--------:|:---:|:------:|:------:|:---:|:----:|:-:|
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| GPT-4o-mini | w/o Search | 21.74 | 22.86 | 19.05 | 34.38 | 19.35 | 23.48 | — |
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| GPT-4o-mini | GPT-4o-mini (as WebPRM) | 24.44 | 22.86 | 26.32 | 33.33 | 15.38 | 24.47 | +0.99 |
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| GPT-4o-mini | WebShepherd-8B | 26.09 | 45.71 | 23.81 | 40.62 | 35.48 | 34.34 | +10.86 |
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| GPT-4o-mini | **WebArbiter-7B** | **37.78** | 42.86 | **36.84** | **46.67** | **38.46** | **40.52** | **+17.04** |
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| GPT-4o | w/o Search | 23.91 | 31.43 | 28.57 | 56.25 | 19.35 | 31.90 | — |
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| GPT-4o | GPT-4o-mini (as WebPRM) | 26.67 | 37.14 | 42.11 | 40.00 | 19.23 | 33.03 | +1.13 |
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| GPT-4o | WebShepherd-8B | 30.43 | 42.86 | 47.62 | 46.88 | 35.48 | 40.65 | +8.75 |
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| GPT-4o | **WebArbiter-7B** | **44.44** | 42.86 | **52.63** | **56.67** | **38.46** | **47.01** | **+15.11** |
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## Quick Start
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "ZYao720/WebArbiter-7B"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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# Construct your prompt following the WebPRMBench format.
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# See https://huggingface.co/datasets/ZYao720/WEBPRMBENCH for examples.
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user_prompt = "..." # evaluation prompt with intent, AXTree, trajectory, two responses
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messages = [{"role": "user", "content": user_prompt}]
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input_ids = tokenizer.apply_chat_template(
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messages, tokenize=True, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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with torch.no_grad():
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output = model.generate(input_ids=input_ids, max_new_tokens=2048, do_sample=False)
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response = tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True)
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print(response)
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```
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**Example output:**
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```xml
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<State>The user is on the DuckDuckGo homepage with a search box visible.
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Relevant AXTree elements: [1] textbox 'Search', [2] button 'Search'.</State>
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<Criteria>1. Goal alignment (weight 0.6) — Does the action advance the search task?
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2. Element reference accuracy (weight 0.25) — Is the referenced element correct?
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3. Efficiency (weight 0.15) — Does the action avoid unnecessary steps?</Criteria>
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<Analysis>Response 1 directly fills the search query into the textbox, which is the
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most direct path to completing the search task. Response 2 clicks an irrelevant link
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that does not contribute to the search goal.</Analysis>
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<Answer>Response 1</Answer>
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```
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## Training Details
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| | Stage 1: Reasoning Distillation | Stage 2: RLVR |
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|---|---|---|
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| Method | Supervised fine-tuning (SFT) | GRPO with binary verifiable rewards |
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| Data | 9,642 teacher-distilled examples | 18,921 preference pairs |
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| Teacher | o3 | — |
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| Base Model | [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Stage 1 checkpoint |
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| Fine-tuning | LoRA (rank 128, lr 8e-4) | FSDP + LoRA (lr 7e-6) |
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| Framework | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) | [veRL](https://github.com/volcengine/verl) |
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| Hardware | 8 × NVIDIA A100-80GB | 8 × NVIDIA A100-80GB |
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| Source Data | [WebPRM Collection](https://huggingface.co/datasets/LangAGI-Lab/WebPRMCollection_preference_pair) (~30k step-level preference pairs from Mind2Web) |
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**Key training insights** (from ablation studies in the paper):
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- Explicit principles are essential — removing them notably degrades performance, especially on out-of-domain environments.
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- Cold-start RL without reasoning distillation is unstable across environments.
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- Reasoning distillation provides stable discrimination, while RL acts as an amplifier that widens the margin between correct and incorrect judgments.
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## Intended Uses
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WebArbiter-7B is designed to:
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- **Evaluate web agent actions**: Given a web state and two candidate actions, determine which better advances the user's task.
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- **Guide trajectory search**: Serve as a reward signal for Best-of-N sampling or tree search during web agent execution.
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- **Provide interpretable feedback**: Generate structured justifications explaining why one action is preferred, useful for debugging and analysis.
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## Limitations
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- **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.
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- **English-only**: Training and evaluation are conducted exclusively in English-language web environments.
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- **Safe-action bias**: The model may sometimes overvalue cautious actions (e.g., hover over click) because the accessibility tree does not encode interaction effects.
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- **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.
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## License
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This model is released under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), following the base model [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
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## Related Resources
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| Resource | Link |
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|----------|------|
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| WebArbiter-8B-Qwen3 (strongest) | [ZYao720/WebArbiter-8B-Qwen3](https://huggingface.co/ZYao720/WebArbiter-8B-Qwen3) |
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| WebArbiter-4B-Qwen3 | [ZYao720/WebArbiter-4B-Qwen3](https://huggingface.co/ZYao720/WebArbiter-4B-Qwen3) |
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| WebArbiter-3B | [ZYao720/WebArbiter-3B](https://huggingface.co/ZYao720/WebArbiter-3B) |
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| WEBPRMBENCH (benchmark) | [ZYao720/WEBPRMBENCH](https://huggingface.co/datasets/ZYao720/WEBPRMBENCH) |
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| Training Data | [ZYao720/WebArbiter-Data](https://huggingface.co/datasets/ZYao720/WebArbiter-Data) |
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| Search Trajectories | [ZYao720/WebArbiter-Trajectories](https://huggingface.co/datasets/ZYao720/WebArbiter-Trajectories) |
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## Citation
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```bibtex
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@misc{zhang2026ZYao720principleguidedreasoningprocess,
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title={WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents},
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author={Yao Zhang and Shijie Tang and Zeyu Li and Zhen Han and Volker Tresp},
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year={2026},
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eprint={2601.21872},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2601.21872},
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}
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```
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
|
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{{- messages[0]['content'] }}
|
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{%- else %}
|
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
|
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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||||
{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
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{%- else %}
|
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{%- if messages[0]['role'] == 'system' %}
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||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
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{%- else %}
|
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
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||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
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||||
{{- tool_call.arguments | tojson }}
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||||
{{- '}\n</tool_call>' }}
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||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
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||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
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||||
{%- endif %}
|
||||
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config.json
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config.json
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{
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"architectures": [
|
||||
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
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||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user