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So-called reasoning models are more efficient but not more capable than regular LLMs, study finds

THE DECODER•Tuesday, November 11, 2025 at 7:49:48 PM
NeutralArtificial Intelligence
So-called reasoning models are more efficient but not more capable than regular LLMs, study finds
The recent study conducted by Tsinghua University and Shanghai Jiao Tong University critically examines the effectiveness of reinforcement learning with verifiable rewards (RLVR) in enhancing the reasoning abilities of large language models (LLMs). The findings indicate that while RLVR makes these models more efficient at executing known solutions, it does not translate into improved reasoning capabilities. This raises important questions about the true advancements in AI training methodologies and their implications for future developments in AI technology. As the field continues to evolve, understanding the limitations of current models is crucial for researchers and developers aiming to create more capable AI systems.
— via World Pulse Now AI Editorial System
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