Trust Region Policy Distillation
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| Authors | Zhengpeng Xie et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2607.04751 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy Distillation (TOP-D), which transforms the notoriously unstable, high-variance On-Policy Distillation (OPD) into a stable training paradigm by dynamically constructing a proximal teacher. Theoretically, we establish a rigorous framework demonstrating that TOP-D inherently controls gradient variance. By providing a formal global convergence analysis alongside a monotonic improvement bound, we mathematically formalize the reliability and stability of the overall training dynamics. Empirically, TOP-D dramatically enhances training stability, sample efficiency, and final performance on mathematical reasoning tasks. More importantly, TOP-D introduces zero additional computational overhead, positioning itself as a promising alternative to the well-established OPD paradigm.
Engineering Breakdown
The Problem
Big goals are hard to achieve all at once; breaking them into small steps is wiser.
The Approach
We present Trust Region Policy Distillation (TOP-D), which transforms the notoriously unstable, high-variance On-Policy Distillation (OPD) into a stable training paradigm by dynamically constructing a proximal teacher.
Key Results
Big goals are hard to achieve all at once; breaking them into small steps is wiser.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Distillation
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