ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation
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. We then propose a reinforcement-learning-based adaptive KL-weighted distillation framework, in which
The continuous growth in LLM complexity necessitates more efficient compression techniques to make them practical and deployable, driving innovation in distillation methods.
This research explores a novel method for more effectively compressing LLMs, which is critical for their widespread adoption and performance in resource-constrained environments.
The proposed ARKD method offers a more balanced approach to knowledge distillation, potentially leading to smaller, more accurate LLMs that retain key generative capabilities.
- · LLM developers
- · Edge AI providers
- · Cloud computing platforms
- · AI application developers
- · Inefficient LLM architectures
More efficient and performant compressed LLMs become available for various applications.
Reduced computational costs and increased accessibility for advanced AI capabilities across industries.
Acceleration of AI integration into everyday devices and systems, fostering new use cases and market segments.
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Read at arXiv cs.AI