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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
When Teacher Guidance Misleads: Reward-Aligned On-Policy Distillation
TL;DR
Reward-Aligned On-Policy Distillation (RA-OPD) improves the training of student models by filtering out misleading guidance from teacher models, enhancing performance on math and code tasks. RA-OPD significantly outperforms standard on-policy distillation methods across multiple benchmarks.
✦ Why It Matters
Implement RA-OPD in your model training to enhance performance and reliability today.
Key Takeaways
- Use RA-OPD to filter misleading teacher guidance.
- Evaluate trajectory-level returns against outcome rewards.
- Test RA-OPD on diverse benchmarks for robust results.
- Optimize student models without extra computational costs.
How It Works
RA-OPD operates by assessing each sampled trajectory's distillation return and comparing it to the actual outcome reward. If the trajectory's guidance does not align with the desired outcome, it is discarded.
This selective filtering ensures that only reliable trajectories are used for training, which helps the student model learn more effectively.