From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
This addresses the challenge of enhancing LLM capabilities efficiently for AI researchers and practitioners, offering a novel fine-tuning paradigm that is not incremental.
The paper tackles the problem of improving large language model performance without scaling data or model size by introducing Aggregation Fine-Tuning (AFT), where models learn to synthesize multiple draft responses into refined answers, resulting in a 41.3% LC win rate on AlpacaEval 2 with a model fine-tuned from Llama3.1-8B-Base.
Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that increasing test-time computational resources can further improve performance. In this work, we introduce Aggregation Fine-Tuning (AFT), a supervised finetuning paradigm where the model learns to synthesize multiple draft responses, referred to as proposals, into a single, refined answer, termed aggregation. At inference time, a propose-and-aggregate strategy further boosts performance by iteratively generating proposals and aggregating them. Empirical evaluations on benchmark datasets show that AFT-trained models substantially outperform standard SFT. Notably, an AFT model, fine-tuned from Llama3.1-8B-Base with only 64k data, achieves a 41.3% LC win rate on AlpacaEval 2, surpassing significantly larger LLMs such as Llama3.1-405B-Instruct and GPT4. By combining sequential refinement and parallel sampling, the propose-and-aggregate framework scales inference-time computation in a flexible manner. Overall, These findings position AFT as a promising approach to unlocking additional capabilities of LLMs without resorting to increasing data volume or model size.