The Markovian Thinker: Architecture-Agnostic Linear Scaling of Reasoning

The introduction of Markovian Thinking marks a significant advancement in the field of reinforcement learning, particularly in training reasoning models. By utilizing a constant-size state, this paradigm allows for linear scaling of reasoning, which is crucial for enhancing model efficiency. Related works, such as Edit Flows, highlight the challenges faced by non-autoregressive models in generating variable-length sequences, emphasizing the importance of flexible structures in model design. Furthermore, the development of fully open language models like Instella showcases a growing trend towards transparency and accessibility in AI research, aligning with the goals of improving reasoning efficiency and performance across various tasks.