Researchers from Meta FAIR, Cornell and Carnegie Mellon have developed TinyLoRA, a fine-tuning method that achieves 91.8% accuracy on GSM8K math benchmark using just 13 trainable parameters on Qwen2.5-7B. The approach uses weight sharing across layers and truncated SVD to scale down trainable parameters dramatically. Reinforcement learning proves 100-1000x more efficient than supervised fine-tuning at extremely low parameter counts. https://www.marktechpost.com/2026/03/24/this-ai-paper-introduces-tinylora-a-13-parameter-fine-tuning-method-that-reaches-91-8-percent-gsm8k-on-qwen2-5-7b/ #AIagent #AI #GenAI #AIResearch #Meta