Reinforcement learning fine-tuning of language model for instruction following and math reasoning
This work addresses the challenge of aligning lightweight language models for specific tasks, offering practical strategies for researchers and practitioners, though it is incremental in combining existing fine-tuning and inference-time techniques.
This study tackled the problem of fine-tuning a compact language model for instruction following and math reasoning, finding that Reinforce Leave-One-Out with DeBERTa reward modeling achieved the best alignment, while synthetic data augmentation and best-of-N sampling significantly improved accuracy in math tasks.
This study investigates the effectiveness of reinforcement learning (RL) fine-tuning techniques on a compact language model (Qwen2.5-0.5B Base) for two challenging tasks: instruction following and mathematical reasoning. We compare supervised fine-tuning (SFT), Direct Preference Optimization (DPO) using preference-labeled data, and Reinforce Leave-One-Out (RLOO) with reward models. Our experiments show that RLOO with DeBERTa reward modeling achieves the best alignment, while DPO provides strong and consistent results. For math reasoing tasks, synthetic data augmentation and best-of-N sampling with an external verifier significantly improve accuracy, showing the potential of combining fine-tuning with inference-time tools. This study highlights key trade-offs and practical strategies for training lightweight, task-aligned small-scale language models.