Large Language Model - The TechBriefs

What if You Could Control How Long a Reasoning Model “Thinks”? CMU Researchers Introduce L1-1.5B: Reinforcement Learning Optimizes AI Thought Process

Reasoning language models have demonstrated the ability to enhance performance by generating longer chain-of-thought sequences during inference, effectively leveraging increased […]

This AI Paper Introduces CODI: A Self-Distillation Framework for Efficient and Scalable Chain-of-Thought Reasoning in LLMs

Chain-of-Thought (CoT) prompting enables large language models (LLMs) to perform step-by-step logical deductions in natural language. While this method has […]

Microsoft and Ubiquant Researchers Introduce Logic-RL: A Rule-based Reinforcement Learning Framework that Acquires R1-like Reasoning Patterns through Training on Logic Puzzles

Large language models (LLMs) have made significant strides in their post-training phase, like DeepSeek-R1, Kimi-K1.5, and OpenAI-o1, showing impressive reasoning […]

This AI Paper from MIT and UCL Introduces a Diagrammatic Approach for GPU-Aware Deep Learning Optimization

Deep learning models, having revolutionized areas of computer vision and natural language processing, become less efficient as they increase in […]

Evaluating Brain Alignment in Large Language Models: Insights into Linguistic Competence and Neural Representations

LLMs exhibit striking parallels to neural activity within the human language network, yet the specific linguistic properties that contribute to […]

Inception Unveils Mercury: The First Commercial-Scale Diffusion Large Language Model

The landscape of generative AI and LLMs has experienced a remarkable leap forward with the launch of Mercury by the […]

This AI Paper Introduces a Parameter-Efficient Fine-Tuning Framework: LoRA, QLoRA, and Test-Time Scaling for Optimized LLM Performance

Large Language Models (LLMs) are essential in fields that require contextual understanding and decision-making. However, their development and deployment come […]