#reasoning models

OpenThoughts introduces a scalable supervised fine-tuning pipeline that significantly enhances reasoning datasets and models, achieving state-of-the-art performance in math, coding, and science domains.

NVIDIA introduces ProRL, a novel reinforcement learning method that extends training duration to unlock new reasoning capabilities in AI models, achieving superior performance across multiple reasoning benchmarks.

USC researchers introduce Tina, a family of compact reasoning models that leverage LoRA and reinforcement learning to deliver strong multi-step reasoning performance at a fraction of typical training costs.

New research shows that specialized reasoning models combined with efficient inference-time scaling methods like majority voting outperform non-reasoning models in complex tasks, offering insights into optimizing computational resources.