Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·7d ago·5 min read
Learning to Revise Reasoning with Segment-wise On-Policy Distillation
TL;DR
Segment-wise On-Policy Distillation (Seg-OPD) enhances reasoning in large language models by allowing students to learn from teacher redrafts of their reasoning segments. This method significantly improves reasoning accuracy, achieving an average relative improvement of 5.22% across various tasks.
✦ Why It Matters
Implement Seg-OPD in your AI training pipeline to enhance reasoning accuracy in language models.
Key Takeaways
- Adopt Seg-OPD for improved reasoning in AI models.
- Utilize uncertainty metrics to select reasoning segments.
- Incorporate teacher redrafts for better learning outcomes.
- Evaluate performance improvements in diverse tasks.
Full Summary
On-policy distillation (OPD) is a technique that enhances the reasoning capabilities of large language models by training them on their own outputs, using detailed feedback from a teacher model. However, traditional token-wise OPD struggles when students generate poor reasoning sequences, as it may inadvertently reinforce these mistakes.
To address this, Seg-OPD introduces a method for revising reasoning by focusing on segments of reasoning rather than individual tokens. By selecting segments based on uncertainty and replacing them with teacher redrafts, Seg-OPD trains students to adopt better reasoning strategies.
Experiments in mathematical reasoning and competitive programming demonstrate that students trained with Seg-OPD achieve higher revision success rates and outperform state-of-the-art methods, with an average improvement of 5.22% in reasoning accuracy. This approach not only enhances the learning process but also provides a structured way to improve reasoning in AI models.