IC-478GPT-4 and GPT-4V achieve approximately 71-73% accuracy in judging whether a web agent trajectory successfully completes a task

Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Jiadai Sun, Xinyue Yang, Yu Yang, Shuntian Yao, Wei Xu, Jie Tang, Yuxiao Dong

SourceWebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

The paper evaluates released LLMs on the task of determining whether a web agent's trajectory successfully completes a given instruction, using the WebArena-Lite test set and 100 manually labeled rollouts. GPT-4 achieves 71.9% on the test set and 71.2% on rollouts; GPT-4 with a captioner achieves 72.6% and 73.3%; GPT-4V achieves 71.2% and 70.5%. All three are substantially below the authors' 8B ORM (80.8% / 79.4%). The evaluation uses the same prompts as Pan et al. (2024) for the captioner and GPT-4V baselines.

Evidence
correlational
Key metric
GPT-4: 71.9% test / 71.2% rollout; Captioner+GPT-4: 72.6% test / 73.3% rollout; GPT-4V: 71.2% test / 70.5% rollout
Caveat
The evaluation is on a single task (binary success/failure of web agent trajectories) in a single environment (WebArena). The 'GPT-4' label in the table may refer to GPT-4-turbo as stated in the text. Results are from a single evaluation setup.
Model
GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report GPT-4V / GPT-4 vision
Datasets
WebArena-Lite [eval]
Related findings
IC-477
Extraction
automatic-extraction