Setlur et al. (2024) found that in mathematical reasoning tasks, using reinforcement learning on a model's incorrect responses is twice as sample-efficient as fine-tuning on correct synthetic answers.

Authors

• Person: Not available Organization: arXiv
  A Survey on the Theory and Mechanism of Large Language Models

Sources

• A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv via serper

Referenced by nodes (2)

• reinforcement learning concept
• fine-tuning concept