Unverified50% confidenceFactExact time
On graph data tasks such as PPI networks, the computational overhead of hyperparameter optimization (HPO) is usually even more striking than model training itself, costing several times or even dozens of times that of model training itself.
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11/6/2026
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UnverifiedStandard PPO requires training an additional value network (critic) of the same scale as the policy model to estimate the baseline, whereas the cost of GRPO is that multiple samples are needed per prompt, increasing sampling cost.78% similarVerifiedThe energy consumption during the training phase of large models is approximately hundreds of times that of the inference phase.74% similarUnverifiedThe training cost of frontier large models consists mainly of three parts: compute (GPU/TPU hours), data (collection and cleaning of high-quality corpora), and labor (research and engineering teams).73% similarUnverifiedReasoning models are extremely costly to train, typically requiring a combination of large-scale reinforcement learning and carefully designed reward models to acquire the capability.73% similarUnverifiedIn the cost structure of large model companies, training costs occur before the model is released and are a one-time but enormous computing investment; inference costs, by contrast, occur continuously with user call volume, and the more users there are and the more frequent the calls, the greater the expenditure.73% similar
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