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Fine-tuning

We explored fine-tuning to optimize the LLM for predicting preciuse DOM selectors. Fine-tuning enhances accuracy in task decomposition, error recovery, and structured output generation. It also helps align the model’s reasoning patterns with the project’s operational goals.

Key Points:

  • Improves domain-specific understanding.
  • Trains on planner-executor interactions for better accuracy.
  • Reduces response variance and hallucinations.
  • Enables customized behavior for enterprise needs.