Adaptive Parallel Reasoning: A Survey of Efficient Inference Scaling

A Berkeley AI Research survey examines adaptive parallel reasoning, in which a reasoning model itself decides when to decompose subtasks, how many concurrent threads to spawn, and how to coordinate them. The post contrasts this with fixed parallelism methods such as self-consistency, Best-of-N, Tree of Thoughts, MCTS, ParaThinker, GroupThink and Hogwild! Inference, which impose the parallel structure externally. Sequential reasoning scales linearly in tokens and latency and risks context-rot, motivating parallel threads that explore independently and concurrently.