Scalable
DeepSeek unveils new technique for smarter, scalable AI reward models
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek AI, a Chinese research lab gaining recognition for its powerful open-source language models such as DeepSeek-R1, has introduced a significant advancement in reward modeling for large language models (LLMs). Their new technique, Self-Principled Critique Tuning…
Salesforce AI Released APIGen-MT and xLAM-2-fc-r Model Series: Advancing Multi-Turn Agent Training with Verified Data Pipelines and Scalable LLM Architectures
AI agents quickly become core components in handling complex human interactions, particularly in business environments where conversations span multiple turns and involve task execution, information extraction, and adherence to specific procedural rules. Unlike traditional chatbots that handle single-turn questions, these agents must hold context over several dialogue exchanges while integrating external data and tool usage….
ZEISS Demonstrates the Power of Scalable Workflows with Ampere Altra and SpinKube — SitePoint
Snapshot Challenge The cost of maintaining a system capable of processing tens of thousands of near-simultaneous requests, but which spends greater than 90 percent of its time in an idle state, cannot be justified. Containerization promised the ability to scale workloads on demand, which includes scaling down when demand is low. Maintaining many pods among…
Scalable Vector Graphics files pose a novel phishing threat
Criminals who conduct phishing attacks over email have ramped up their abuse of a new threat vector designed to bypass existing anti-spam and anti-phishing protection: The use of a graphics file format called SVG. The attacks, which begin with email messages that have .svg file attachments, started to spread late last year, and have ramped…
Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning Capabilities
Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get…
How Is Kubernetes Revolutionizing Scalable AI Workflows in LLMOps?
Introduction The advent of large language models (LLMs) has transformed artificial intelligence, enabling organizations to innovate and solve complex problems at an unprecedented scale. From powering advanced chatbots to enhancing natural language understanding, LLMs have redefined what AI can achieve. However, managing the lifecycle of LLMs—from data pre-processing and training to deployment and monitoring—presents unique…
This AI Paper Explores Reinforced Learning and Process Reward Models: Advancing LLM Reasoning with Scalable Data and Test-Time Scaling
Scaling the size of large language models (LLMs) and their training data have now opened up emergent capabilities that allow these models to perform highly structured reasoning, logical deductions, and abstract thought. These are not incremental improvements over previous tools but mark the journey toward reaching Artificial general intelligence (AGI). Training LLMs to reason well…