Web-Shepherd: Advancing PRMs for Reinforcing Web Agents
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908676246208512 |
|---|---|
| author | Chae, Hyungjoo Kim, Sunghwan Cho, Junhee Kim, Seungone Moon, Seungjun Hwangbo, Gyeom Lim, Dongha Kim, Minjin Hwang, Yeonjun Gwak, Minju Choi, Dongwook Kang, Minseok Im, Gwanhoon Cho, ByeongUng Kim, Hyojun Han, Jun Hee Kwon, Taeyoon Kim, Minju Kwak, Beong-woo Kang, Dongjin Yeo, Jinyoung |
| author_facet | Chae, Hyungjoo Kim, Sunghwan Cho, Junhee Kim, Seungone Moon, Seungjun Hwangbo, Gyeom Lim, Dongha Kim, Minjin Hwang, Yeonjun Gwak, Minju Choi, Dongwook Kang, Minseok Im, Gwanhoon Cho, ByeongUng Kim, Hyojun Han, Jun Hee Kwon, Taeyoon Kim, Minju Kwak, Beong-woo Kang, Dongjin Yeo, Jinyoung |
| contents | Web navigation is a unique domain that can automate many repetitive real-life tasks and is challenging as it requires long-horizon sequential decision making beyond typical multimodal large language model (MLLM) tasks. Yet, specialized reward models for web navigation that can be utilized during both training and test-time have been absent until now. Despite the importance of speed and cost-effectiveness, prior works have utilized MLLMs as reward models, which poses significant constraints for real-world deployment. To address this, in this work, we propose the first process reward model (PRM) called Web-Shepherd which could assess web navigation trajectories in a step-level. To achieve this, we first construct the WebPRM Collection, a large-scale dataset with 40K step-level preference pairs and annotated checklists spanning diverse domains and difficulty levels. Next, we also introduce the WebRewardBench, the first meta-evaluation benchmark for evaluating PRMs. In our experiments, we observe that our Web-Shepherd achieves about 30 points better accuracy compared to using GPT-4o on WebRewardBench. Furthermore, when testing on WebArena-lite by using GPT-4o-mini as the policy and Web-Shepherd as the verifier, we achieve 10.9 points better performance, in 10 less cost compared to using GPT-4o-mini as the verifier. Our model, dataset, and code are publicly available at LINK. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Web-Shepherd: Advancing PRMs for Reinforcing Web Agents Chae, Hyungjoo Kim, Sunghwan Cho, Junhee Kim, Seungone Moon, Seungjun Hwangbo, Gyeom Lim, Dongha Kim, Minjin Hwang, Yeonjun Gwak, Minju Choi, Dongwook Kang, Minseok Im, Gwanhoon Cho, ByeongUng Kim, Hyojun Han, Jun Hee Kwon, Taeyoon Kim, Minju Kwak, Beong-woo Kang, Dongjin Yeo, Jinyoung Computation and Language Web navigation is a unique domain that can automate many repetitive real-life tasks and is challenging as it requires long-horizon sequential decision making beyond typical multimodal large language model (MLLM) tasks. Yet, specialized reward models for web navigation that can be utilized during both training and test-time have been absent until now. Despite the importance of speed and cost-effectiveness, prior works have utilized MLLMs as reward models, which poses significant constraints for real-world deployment. To address this, in this work, we propose the first process reward model (PRM) called Web-Shepherd which could assess web navigation trajectories in a step-level. To achieve this, we first construct the WebPRM Collection, a large-scale dataset with 40K step-level preference pairs and annotated checklists spanning diverse domains and difficulty levels. Next, we also introduce the WebRewardBench, the first meta-evaluation benchmark for evaluating PRMs. In our experiments, we observe that our Web-Shepherd achieves about 30 points better accuracy compared to using GPT-4o on WebRewardBench. Furthermore, when testing on WebArena-lite by using GPT-4o-mini as the policy and Web-Shepherd as the verifier, we achieve 10.9 points better performance, in 10 less cost compared to using GPT-4o-mini as the verifier. Our model, dataset, and code are publicly available at LINK. |
| title | Web-Shepherd: Advancing PRMs for Reinforcing Web Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.15277 |