Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
BAIR proposes Adaptive Parallel Reasoning, letting models dynamically decompose and parallelize subtasks. This addresses inference-time scaling bottlenecks by running concurrent reasoning threads independently, reducing latency and context-window degradation compared to sequential approaches.