What Generative Search Engines Like and How to Optimize Web Content Cooperatively
Paper
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2510.11438
•
Published
•
10
AutoGEO_mini_Qwen1.7B_GEOBench
A lightweight web-document rewriting model fine-tuned with GRPO (reinforcement learning) from Qwen3-1.7B, developed as part of the AutoGEO framework introduced in:
WHAT GENERATIVE SEARCH ENGINES LIKE AND HOW TO OPTIMIZE WEB CONTENT COOPERATIVELY
Paper (arXiv): https://arxiv.org/abs/2510.11438
What this model does
AutoGEO_mini_Qwen1.7B_GEOBench rewrites raw web documents into improved versions that are better aligned with generative search engines’ preferences for GEO-Bench dataset.
In our experiments/usage:
- The total cost is about 0.0071× the cost of gemini-2.5-pro for comparable rewriting workloads.
- Rewritten documents achieve significant improvements in GEO metrics.
Training summary
- Base model: Qwen3-1.7B
- Method: GRPO-based reinforcement learning fine-tuning
- Task: Rewrite original web documents to improve GEO metrics (per the AutoGEO framework in the paper above)
Repository contents
This repository includes the standard inference artifacts (e.g., model.safetensors, config.json, tokenizer.json, chat_template.jinja, etc.) required to load and run the model with transformers.
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