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Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
🤖AI Summary
Researchers propose Generalized On-Policy Distillation (G-OPD), a new AI training framework that improves upon standard on-policy distillation by introducing flexible reference models and reward scaling factors. The method, particularly ExOPD with reward extrapolation, enables smaller student models to surpass their teacher's performance in math reasoning and code generation tasks.
Key Takeaways
- →G-OPD extends standard on-policy distillation with flexible reference models and reward scaling factors for better AI training.
- →ExOPD with reward scaling factor greater than 1 consistently outperforms standard OPD across different model size pairings.
- →Student models can surpass teacher performance when merging knowledge from domain-specific experts using ExOPD.
- →Reward correction using teacher's pre-RL base model as reference improves strong-to-weak distillation performance.
- →The framework demonstrates superior results in math reasoning and code generation benchmarks.
#machine-learning#ai-training#model-distillation#reinforcement-learning#arxiv#research#performance-optimization#neural-networks
Read Original →via arXiv – CS AI
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