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Large language models (LLMs) such as GPT, Gemini and Claude use huge training data sets and complex architectures to generate high quality answers. However, optimization of their inference time calculation remains challenging as rising model size leads to higher calculation costs. Researchers continue to explore strategies that maximize efficiency while maintaining or improving model performance. … Read more

Language models (LMS) are significant progress through increased calculation power during training, primarily through large -scale self -monitored pre -entering. While this approach has provided powerful models, a new paradigm called test time scaling has emerged focusing on improving performance by increasing the calculation at inference time. Openai’s O1 model has validated this approach showing … Read more

Large Language Models (LLMs) have shown remarkable reasoning functions in mathematical problem solving, logical inference and programming. However, their effectiveness is often conditioned by two approaches: monitored fine tuning (SFT) with human-annoted reasoning chains and Search strategies in inference time Guided by external verifiers. While monitored fine -tuning offers structured reasoning, it requires significant annotation … Read more