Hybrid Learning for LLM Fine-tuning
Analysis
This paper proposes a unified framework for fine-tuning Large Language Models (LLMs) by combining Imitation Learning and Reinforcement Learning. The key contribution is a decomposition of the objective function into dense and sparse gradients, enabling efficient GPU implementation. This approach could lead to more effective and efficient LLM training.
Key Takeaways
- •Combines Imitation Learning and Reinforcement Learning for LLM fine-tuning.
- •Decomposes the objective function into dense and sparse gradients.
- •Provides a closed-form formula for the dense gradient, enabling efficient GPU implementation.
Reference
“The Dense Gradient admits a closed-form logit-level formula, enabling efficient GPU implementation.”