Berkeley Artificial Intelligence Research

Adaptive Parallel Reasoning: A New Paradigm for Efficient AI Inference Scaling

Adaptive Parallel Reasoning (APR) is a paradigm where LLMs learn to dynamically decide when and how to parallelize reasoning subtasks, avoiding redundant computation and improving efficiency. Unlike fixed parallelism methods, APR allows models to choose decomposition strategies, thread counts, and coordination per problem, leveraging reinforcement learning and special control tokens.

Berkeley Artificial Intelligence Research