Generative retrieval (GR) retrieves documents by directly generating document identifiers (DocIDs) from queries using a sequence-to-sequence model. Recent reasoning-enhanced GR methods improve retrieval effectiveness by generating intermediate reasoning before DocID decoding. However, they typically apply reasoning uniformly to all queries, even though many queries can already be handled effectively through direct DocID decoding. This introduces unnecessary token generation and retrieval latency when reasoning provides little additional benefit. In this work, we propose Generative Retrieval with Adaptive Reasoning (GEAR), a retrieval framework that dynamically allocates reasoning computation based on query-specific needs. Instead of treating reasoning as a fixed pre-retrieval procedure, GEAR learns when to reason, what retrieval-oriented context to generate, and when to transition from free-form generation to constrained DocID decoding. This adaptive behavior is implemented through a learnable retrieval transition policy optimized jointly for retrieval quality and reasoning cost. Experiments on NQ, MS MARCO, and TREC DL 2022 show that GEAR maintains retrieval effectiveness comparable to strong reasoning-enhanced GR methods while substantially reducing reasoning overhead, triggering non-trivial reasoning for only 37.1% of queries and reducing both generated tokens and retrieval latency.