Scaling Reasoning Efficiently via Relaxed On-Policy Distillation
arXiv:2603.11137v1 Announce Type: new Abstract: On-policy distillation is pivotal for transferring reasoning capabilities to capacity-constrained models, yet remains prone to instability and negative transfer. We show that on-policy distillation can be interpreted, both theoretically and empirically, as a form of policy optimization, where the teacher-student log-likelihood ratio acts as a token reward. From this insight, we introduce REOPOLD (Relaxed On-Policy Distillation) a framework that stabilizes optimization by relaxing the strict imitation constraints of standard on-policy distillation. Specifically, REOPOLD temperately and selectively leverages rewards from the teacher through mixture-based reward clipping, entropy-based token-level dynamic sampling, and a unified exploration-to-refinement training strategy. Empirically, REOPOLD surpasses its baselines with superior sample efficiency during training and enhanced test-time scaling at inference, across mathematical, visual, and
arXiv:2603.11137v1 Announce Type: new Abstract: On-policy distillation is pivotal for transferring reasoning capabilities to capacity-constrained models, yet remains prone to instability and negative transfer. We show that on-policy distillation can be interpreted, both theoretically and empirically, as a form of policy optimization, where the teacher-student log-likelihood ratio acts as a token reward. From this insight, we introduce REOPOLD (Relaxed On-Policy Distillation) a framework that stabilizes optimization by relaxing the strict imitation constraints of standard on-policy distillation. Specifically, REOPOLD temperately and selectively leverages rewards from the teacher through mixture-based reward clipping, entropy-based token-level dynamic sampling, and a unified exploration-to-refinement training strategy. Empirically, REOPOLD surpasses its baselines with superior sample efficiency during training and enhanced test-time scaling at inference, across mathematical, visual, and agentic tool-use reasoning tasks. Specifically, REOPOLD outperforms recent RL approaches achieving 6.7~12x greater sample efficiency and enables a 7B student to match a 32B teacher in visual reasoning with a ~3.32x inference speedup.
Executive Summary
The article introduces REOPOLD, a framework for on-policy distillation that stabilizes optimization by relaxing strict imitation constraints. REOPOLD achieves superior sample efficiency and enhanced test-time scaling across various reasoning tasks, outperforming recent RL approaches and enabling a 7B student to match a 32B teacher in visual reasoning with a significant inference speedup. This breakthrough has significant implications for transferring reasoning capabilities to capacity-constrained models, making it a crucial contribution to the field of artificial intelligence.
Key Points
- ▸ REOPOLD framework for on-policy distillation
- ▸ Relaxing strict imitation constraints for stability
- ▸ Superior sample efficiency and test-time scaling
Merits
Improved Sample Efficiency
REOPOLD achieves 6.7-12x greater sample efficiency compared to recent RL approaches, making it a more efficient method for training models.
Enhanced Test-Time Scaling
REOPOLD enables a 7B student to match a 32B teacher in visual reasoning with a ~3.32x inference speedup, demonstrating its potential for practical applications.
Demerits
Limited Theoretical Analysis
The article primarily focuses on empirical results, with limited theoretical analysis of the REOPOLD framework, which may raise questions about its generalizability and robustness.
Expert Commentary
The introduction of REOPOLD marks a significant advancement in on-policy distillation, offering a more efficient and scalable approach to transferring reasoning capabilities. The empirical results demonstrate the potential of REOPOLD for practical applications, and its implications for AI research and policy are substantial. However, further theoretical analysis and evaluation of REOPOLD's limitations are necessary to fully understand its potential and robustness. As the field of AI continues to evolve, the development of efficient knowledge transfer techniques like REOPOLD will play a crucial role in shaping the future of AI research and applications.
Recommendations
- ✓ Further theoretical analysis of the REOPOLD framework to understand its generalizability and robustness
- ✓ Evaluation of REOPOLD's potential applications in various domains, such as computer vision and natural language processing