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$\mathcal{X}$-KD: General Experiential Knowledge Distillation for Large Language Models

arXiv:2602.12674v1 Announce Type: new Abstract: Knowledge Distillation (KD) for Large Language Models (LLMs) has become increasingly important as models grow in size and complexity. While existing distillation approaches focus on imitating teacher behavior, they often overlook the original learning environment that shaped the teacher's knowledge. Inspired by the experiential learning theory and inverse reinforcement learning, we propose Experiential Knowledge Distillation ($\mathcal{X}$-KD), a novel and general framework that enables student models to learn in the teacher's original learning environment. $\mathcal{X}$-KD adopts the Approximated Variational Reward Imitation Learning (AVRIL) framework to jointly model the teacher's original reward function and perform policy distillation, encouraging consistency between the student policy and the original reward function. Our derivation demonstrates that $\mathcal{X}$-KD follows the supervised learning framework and applies to both sequ

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Yuang Cai, Yuyu Yuan · · 1 min read · 43 views
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arXiv:2602.12674v1 Announce Type: new Abstract: Knowledge Distillation (KD) for Large Language Models (LLMs) has become increasingly important as models grow in size and complexity. While existing distillation approaches focus on imitating teacher behavior, they often overlook the original learning environment that shaped the teacher's knowledge. Inspired by the experiential learning theory and inverse reinforcement learning, we propose Experiential Knowledge Distillation ($\mathcal{X}$-KD), a novel and general framework that enables student models to learn in the teacher's original learning environment. $\mathcal{X}$-KD adopts the Approximated Variational Reward Imitation Learning (AVRIL) framework to jointly model the teacher's original reward function and perform policy distillation, encouraging consistency between the student policy and the original reward function. Our derivation demonstrates that $\mathcal{X}$-KD follows the supervised learning framework and applies to both sequence-level and divergence-based distillation methods, underlining the simplicity and flexibility of our approach. Empirical results show that $\mathcal{X}$-KD outperforms the generalized KD and MiniLLM baselines on abstractive summarization, machine translation, and arithmetic reasoning tasks. Additionally, $\mathcal{X}$-KD achieves better performance-diversity trade-off and data efficiency than baseline KD approaches.

Executive Summary

The article introduces Experiential Knowledge Distillation (\mathcal{X}-KD), a novel framework for distilling knowledge from large language models (LLMs) by leveraging the teacher's original learning environment. Inspired by experiential learning theory and inverse reinforcement learning, \mathcal{X}-KD uses the Approximated Variational Reward Imitation Learning (AVRIL) framework to model the teacher's reward function and perform policy distillation. The approach is shown to be flexible, applicable to various distillation methods, and outperforms baseline approaches in tasks such as abstractive summarization, machine translation, and arithmetic reasoning. The study highlights the importance of considering the original learning context in knowledge distillation, offering a more effective and efficient method for training student models.

Key Points

  • ▸ Introduction of \mathcal{X}-KD framework for knowledge distillation in LLMs.
  • ▸ Use of AVRIL to model the teacher's reward function and perform policy distillation.
  • ▸ Applicability to both sequence-level and divergence-based distillation methods.
  • ▸ Outperformance of baseline approaches in various tasks.
  • ▸ Emphasis on the importance of the original learning environment in knowledge distillation.

Merits

Innovative Approach

\mathcal{X}-KD introduces a novel method that considers the teacher's original learning environment, which is a significant advancement over traditional knowledge distillation techniques.

Flexibility

The framework is shown to be applicable to various distillation methods, making it a versatile tool for different scenarios.

Empirical Success

The approach outperforms baseline methods in multiple tasks, demonstrating its practical effectiveness.

Demerits

Complexity

The integration of inverse reinforcement learning and the AVRIL framework may add complexity to the implementation process.

Generalizability

While the study shows promising results, further research is needed to assess the generalizability of \mathcal{X}-KD across a broader range of tasks and model architectures.

Expert Commentary

The article presents a significant advancement in the field of knowledge distillation for large language models. By incorporating the teacher's original learning environment through the AVRIL framework, \mathcal{X}-KD offers a more nuanced and effective approach to training student models. The empirical results demonstrate its superiority over baseline methods, particularly in tasks requiring complex reasoning and understanding. However, the complexity introduced by the integration of inverse reinforcement learning may pose challenges for practical implementation. Future research should focus on simplifying the framework and validating its generalizability across a wider range of applications. The study's emphasis on the importance of the original learning context in knowledge distillation is a valuable contribution to the field and underscores the need for more sophisticated methods in this area.

Recommendations

  • ✓ Further research should explore the generalizability of \mathcal{X}-KD across different model architectures and tasks.
  • ✓ Efforts should be made to simplify the implementation of the framework to make it more accessible for practical applications.

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