Open Training Recipes for Reasoning in Language Models
In today’s rapidly evolving AI landscape, the remarkable progress we’ve witnessed is largely attributed to open scientific research and fully open models. However, as time progresses, more and more research and development work is becoming increasingly closed off. We still need to delve deeper into how language models work, improve their capabilities, and make them safer, more efficient, and more reliable. Simultaneously, we need to extend language models’ abilities beyond text into domains like healthcare, science, and even complex decision-making processes. Most importantly, we must bring these models into real-world applications, ensuring they are deployable, interpretable, and effectively mitigate biases and risks. ...