Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM

A recent study on Test-Time Scaling (TTS) advances the understanding of optimizing computation during inference for large language models (F1, F2). Unlike prior research that concentrated on fixed model architectures, this work generalizes TTS by treating it as an optimizable graph, enabling flexible combinations of models and architectures tailored to specific tasks (A1, F3). This approach allows for more efficient AI applications by dynamically adjusting computational resources at test time rather than relying on static configurations. The study’s findings support the positive impact of generalizing test-time compute-optimal scaling, highlighting its potential to enhance performance in diverse scenarios (A1). This development aligns with ongoing research efforts to improve large language model efficiency and adaptability, as noted in related recent literature (connected coverage). Overall, the study contributes a novel framework for leveraging model flexibility during inference, which may influence future AI system designs.

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