Reinforcement learning trains small models for text-to-SPARQL generation

Researchers have explored using reinforcement learning to train smaller language models for zero-shot Text-to-SPARQL generation, a task crucial for knowledge graph question answering. They applied Group-Relative Policy Optimization (GRPO) to the Qwen3-1.7B model, utilizing execution feedback and answer-level rewards instead of requiring gold query annotations. The GRPO-trained models showed significant improvement over a zero-shot baseline, demonstrating the viability of outcome-based reinforcement learning for this task when full supervision is unavailable. AI

影响 Demonstrates a viable method for training smaller models on complex tasks without extensive labeled data, potentially lowering barriers to knowledge graph querying.

排序理由 Academic paper detailing a novel approach to text-to-SPARQL generation using reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• DBLP-QuAD
• GRPO
• Qwen3-1.7B

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