LLMSelf-ReflectionLoRAFine-Tuning2025
Low-Resource Self-Reflective Fine-Tuning of Compact Language Models Using LoRA
By Gibran Khan Tareen, Mir Farhan Nawaz
Teaching a tiny LLM to self-reflect and correct its own mistakes using a small structured reflection dataset and LoRA fine-tuning — showing better reasoning with minimal compute.
Abstract
This research explores low-resource fine-tuning techniques for compact language models, investigating how self-reflection capabilities can be instilled through structured LoRA fine-tuning. Using a carefully curated reflection dataset, we demonstrate that models as small as 0.5B parameters can exhibit meaningful self-correction behaviours with significantly reduced computational requirements.
Key Findings
- 0.5B parameter models can achieve meaningful self-reflection with LoRA fine-tuning
- Structured reflection datasets are more efficient than general instruction tuning for this capability
- Compute requirements reduced significantly vs. full fine-tuning approaches
- HAI-ReflectMini-0.5B released open-source on HuggingFace for community use