How to Train Your LLM Web Agent: A Statistical Diagnosis

Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary agents. Bridging this gap is key to enabling customizable, efficient, and privacy-preserving agents. Two challenges hinder progress: the reproducibility issues in RL and LLM agent training, where results often depend on sensitive factors like seeds and decoding parameters, and the focus of prior work on single-step tasks, overlooking the complexities of web-based, multi-step decision-making. We address these gaps by providing a statistically driven study of training LLM agents for web tasks. Our two-stage pipeline combines imitation learning from a Llama 3. 3 70B teacher with on-policy fine-tuning via Group Relative Policy Optimization (GRPO) on a Llama 3. 1 8B student. Through 240 configuration sweeps and rigorous bootstrapping, we chart the first compute allocation curve for open-source LLM web agents. Our findings show that dedicating one-third of compute to teacher traces and the rest to RL improves MiniWoB++ success by 6 points and closes 60\% of the gap to GPT-4o on WorkArena, while cutting GPU costs by 45\%. We introduce a principled hyperparameter sensitivity analysis, offering actionable guidelines for robust and cost-effective agent training.

Authors

• Dheeraj Vattikonda
• Santhoshi Ravichandran
• Emiliano Penaloza
• Hadi Nekoei
• Thibault de Chezelles
• Megh Thakkar
• Nicolas Gontier
• Miguel Muñoz-Mármol
• Sahar Omidi Shayegan
• Stefania Raimondo
• Steve (Xue) Liu
• Alexandre Drouin
• Alexandre Piche
• Alexandre Lacoste
• Massimo Caccia

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