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Reinforcement-aware Knowledge Distillation for LLM Reasoning
arXiv – CS AI|Zhaoyang Zhang, Shuli Jiang, Yantao Shen, Yuting Zhang, Dhananjay Ram, Shuo Yang, Zhuowen Tu, Wei Xia, Stefano Soatto||6 views
🤖AI Summary
Researchers propose RL-aware distillation (RLAD), a new method to efficiently transfer knowledge from large language models to smaller ones during reinforcement learning training. The approach uses Trust Region Ratio Distillation (TRRD) to selectively guide student models only when it improves policy updates, outperforming existing distillation methods across reasoning benchmarks.
Key Takeaways
- →RLAD addresses distribution mismatch and objective interference issues in traditional knowledge distillation methods combined with reinforcement learning.
- →Trust Region Ratio Distillation (TRRD) replaces standard KL regularization with a PPO/GRPO-style likelihood-ratio objective for better performance.
- →The method performs selective imitation, guiding student models toward teachers only when beneficial for current policy updates.
- →RLAD consistently outperforms offline distillation, standard GRPO, and KL-based teacher-student distillation across logic reasoning and math benchmarks.
- →The approach naturally balances exploration, exploitation, and imitation without requiring careful loss balancing.
#reinforcement-learning#knowledge-distillation#llm#reasoning#model-compression#machine-learning#ai-efficiency
Read Original →via arXiv – CS AI
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