Adaptive Parallel Reasoning: A New Paradigm for Efficient Scaling of AI Inference
Researchers at BAIR analyze the shift from fixed parallel strategies to adaptive parallel reasoning (APR), where the model itself decides when and how to parallelize subtasks. APR reduces the redundancy and latency inherent in sequential reasoning and does not require manual specification of the parallelism structure.
Berkeley Artificial Intelligence Research
BAIR (Berkeley AI)27.07 · 14:04