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Salesforce AI Research Introduces Reward-Guided Speculative Decoding (RSD): A Novel Framework that Improves the Efficiency of Inference in Large Language Models (LLMs) Up To 4.4× Fewer FLOPs #AIResearch #MachineLearning #Efficiency #Salesforce #Language… https://t.co/2jMQ7t0Wnl https://t.co/R77732T2QK
Salesforce AI Research Introduces Reward-Guided Speculative Decoding (RSD): A Novel Framework that Improves the Efficiency of Inference in Large Language Models (LLMs) Up To 4.4× Fewer FLOPs Salesforce AI Research Introduces Reward-Guided Speculative Decoding (RSD), a novel… https://t.co/cebRsjV9Oo
AI progress continues to shine as a 7B LLM just outperformed o1 and DeepSeek-R1 with higher inference efficiency 🚀 Learn more on this week's #1 trending paper: Can 1B LLM Surpass 405B LLM? Test-Time Scaling (TTS) continues to promise enhanced reasoning abilities of LLMs. https://t.co/yeoPxU6d2C
Salesforce AI Research has unveiled a new framework called Reward-Guided Speculative Decoding (RSD), which enhances the inference efficiency of large language models (LLMs) by up to 4.4 times while also improving accuracy. This breakthrough approach is designed to reduce the number of floating point operations (FLOPs) required during LLM inference, potentially transforming how AI models are deployed in various applications. The introduction of RSD marks a significant advancement in the field of AI, particularly in improving reasoning capabilities and computational efficiency. Additionally, a 1.5 billion parameter model named DeepScaleR has reportedly outperformed OpenAI's O1-preview in complex mathematical reasoning tasks, showcasing the rapid evolution of AI technologies and their increasing capabilities.
