How does the S* hybrid test-time scaling framework affect the inference efficiency (measured in average latenc

Abstract

Abstract: We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. To maximize computational capacity, we integrate it with Mixture of Experts (MoE), creating a model with 32 experts and 456 billion total parameters, of which 45.9 billion are activated for each token. We develop an optimized parallel strategy and highly efficient computation-communication overlap techniques for MoE and lightning attention. This app

Research Question

How does the S* hybrid test-time scaling framework affect the inference efficiency (measured in average latency and FLOPs per correct solution) relative to standard parallel scaling when evaluated on self-invoking code generation tasks from HumanEval Pro and MBPP Pro?

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