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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 11·5 min read
WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training
TL;DR
WDL-OPD introduces a novel method for on-policy distillation that stabilizes training by using two policies: an anchor and an auxiliary. This approach significantly improves performance in tasks like code generation and mathematical problem-solving, achieving notable accuracy increases.
✦ Why It Matters
Implement WDL-OPD in your training pipeline to enhance model performance and stability in dynamic environments.
Key Takeaways
- Utilize WDL-OPD for improved model training stability.
- Experiment with anchor and auxiliary policies for better performance.
- Monitor accuracy improvements in specific tasks like MATH500.
- Consider the implications of curriculum and initialization on results.
How It Works
WDL-OPD employs two policies: an anchor policy that generates rollouts and an auxiliary policy that evaluates the same states. The key innovation is the matching of a geometric mixture of token distributions from both policies to a frozen teacher model using reverse Kullback-Leibler divergence.
This approach stabilizes the training process by decoupling the updates of the policies and the states, allowing for more effective learning.