186 lines
9.3 KiB
Markdown
186 lines
9.3 KiB
Markdown
---
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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/Qwen2.5-3B-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-3B
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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: 83.65
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- name: Avg BoN Accuracy
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type: accuracy
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value: 59.06
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---
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<div align="center">
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# WebArbiter-3B
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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-3B** is a 3B reasoning Process Reward Model (PRM) for web agents, built on [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-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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Despite its compact size, WebArbiter-3B achieves an **Avg. BoN Acc of 59.06%** on [WEBPRMBENCH](https://huggingface.co/datasets/ZYao720/WEBPRMBENCH), outperforming the previous SOTA WebPRM (WebShepherd-3B) by **15.5 points** and surpassing all open-source LLM-as-judge baselines up to 70B parameters. For the strongest variant, see [WebArbiter-7B](https://huggingface.co/ZYao720/WebArbiter-7B).
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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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- **Efficient and deployable**: Strong performance at 3B parameters, suitable for resource-constrained deployment scenarios.
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## Results on WebPRMBench
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Models marked with ⋆ are ours. **Bold** = best at comparable scale.
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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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| *Open-source LLM-as-judge* | | | | | | | | | | |
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| Qwen2.5-3B-Instruct | 76.46 | 36.93 | 60.32 | 15.42 | 75.83 | 33.33 | 64.45 | 19.34 | 69.27 | 26.76 |
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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 (3B)* | | | | | | | | | | |
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| WebShepherd-3B | 87.50 | 65.21 | 68.16 | 41.29 | 66.67 | 46.67 | 50.00 | 21.23 | 68.08 | 43.60 |
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| ⋆ **WebArbiter-3B** | **93.32** | **78.42** | **81.97** | **56.22** | **78.33** | 46.67 | **81.01** | **54.81** | **83.65** | **59.06** |
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| *WebPRMs (7B+)* | | | | | | | | | | |
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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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WebArbiter-3B outperforms WebShepherd-8B (a much larger 8B model) on Avg. BoN Acc (59.06 vs 43.28), demonstrating the efficiency of the principle-guided reasoning approach.
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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-3B"
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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 | [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) | Stage 1 checkpoint |
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| Fine-tuning | LoRA (rank 128, lr 8e-4) | FSDP + LoRA (lr 9e-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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## Intended Uses
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WebArbiter-3B 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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- **Resource-efficient deployment**: Suitable for scenarios where 7B+ models are too large, while still significantly outperforming larger checklist-based WebPRMs.
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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 [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-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-7B | [ZYao720/WebArbiter-7B](https://huggingface.co/ZYao720/WebArbiter-7B) |
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| WebArbiter-4B-Qwen3 | [ZYao720/WebArbiter-4B-Qwen3](https://huggingface.co/ZYao720/WebArbiter-4B-Qwen3) |
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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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