ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation
📰 ArXiv cs.AI
Learn how ARKD improves text generation by balancing primary distribution fitting and long-tail probability modeling using adaptive reinforcement learning and bidirectional KL divergence distillation
Action Steps
- Apply knowledge distillation to compress Large Language Models (LLMs) using ARKD
- Use adaptive reinforcement learning to guide the distillation process
- Configure bidirectional KL divergence (FKL/RKL) for distribution alignment
- Test the performance of ARKD on text generation tasks
- Compare the results with other distillation methods to evaluate the effectiveness of ARKD
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve the quality and generalization of their text generation models
Key Insight
💡 Adaptive reinforcement learning and bidirectional KL divergence distillation can improve the balance between primary distribution fitting and long-tail probability modeling in text generation
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📚 Improve text generation with ARKD: adaptive reinforcement learning-guided bidirectional KL divergence distillation
Key Takeaways
Learn how ARKD improves text generation by balancing primary distribution fitting and long-tail probability modeling using adaptive reinforcement learning and bidirectional KL divergence distillation
Full Article
Title: ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation
Abstract:
arXiv:2606.29869v1 Announce Type: cross Abstract: Knowledge distillation (KD) is a key technique for compressing Large Language Models (LLMs), yet methods relying on a single KL objective often fail to balance primary distribution fitting with long-tail probability modeling, limiting both generation quality and generalization. To address this, we analyze the complementary roles of forward and reverse KL divergence (FKL/RKL) in distribution alignment from theoretical and empirical perspectives. W
Abstract:
arXiv:2606.29869v1 Announce Type: cross Abstract: Knowledge distillation (KD) is a key technique for compressing Large Language Models (LLMs), yet methods relying on a single KL objective often fail to balance primary distribution fitting with long-tail probability modeling, limiting both generation quality and generalization. To address this, we analyze the complementary roles of forward and reverse KL divergence (FKL/RKL) in distribution alignment from theoretical and empirical perspectives. W
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