RAISE: Reinforced Adaptive Instruction Selection For Large Language Models
This work addresses the challenge of inefficient instruction fine-tuning for LLM developers, offering a task-specific optimization method that is more effective than heuristic-based approaches.
The paper tackles the problem of selecting high-quality instructions for fine-tuning large language models by proposing RAISE, a reinforced adaptive framework that dynamically selects instructions during training, achieving superior performance with only 1% of training steps compared to full-data training.
In the instruction fine-tuning of large language models (LLMs), it is widely recognized that a few high-quality instructions are superior to a large number of low-quality instructions. At present, many instruction selection methods have been proposed, but most of these methods select instruction based on heuristic quality metrics, and only consider data selection before training. These designs lead to insufficient optimization of instruction fine-tuning, and fixed heuristic indicators are often difficult to optimize for specific tasks. Therefore, we design a dynamic, task-objective-driven instruction selection framework RAISE(Reinforced Adaptive Instruction SElection), which incorporates the entire instruction fine-tuning process into optimization, selecting instructions at each step based on the expected impact of each instruction on model performance improvement. Our approach is well interpretable and has strong task-specific optimization capabilities. By modeling dynamic instruction selection as a sequential decision-making process, we use RL to train our selection strategy. Extensive experiments and result analysis prove the superiority of our method compared with other instruction selection methods. Notably, RAISE achieves superior performance by updating only 1% of the training steps compared to full-data training, demonstrating its efficiency and effectiveness.