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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 13·5 min read
REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation
TL;DR
REOPD introduces a reliability-adaptive reward extrapolation framework for on-policy distillation, enhancing training stability by using token-wise coefficients. It outperforms existing methods in various settings, demonstrating effective adaptation across domains.
✦ Why It Matters
Implement REOPD in your OPD workflows to improve training stability and performance across diverse tasks.
Key Takeaways
- Adopt REOPD for more stable training in OPD scenarios.
- Utilize token-level compatibility weights for better alignment.
- Avoid global coefficients to reduce reward hacking risks.
- Test REOPD across different domains for optimal performance.
How It Works
REOPD enhances on-policy distillation by introducing a token-wise coefficient that adapts based on the reliability of teacher guidance. It combines a compatibility weight for each token with an adaptive budget at the batch level, allowing the model to focus on more reliable teacher references while maintaining overall alignment.