ATLAS traces RLVR data to 20 atomic sources, most datasets are variants, and DAPO++ curated with SCA improves RLVR performance while Q predicts training effectiveness.
Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs
3 Pith papers cite this work. Polarity classification is still indexing.
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This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.
The GPT family has shifted from scaled text predictors to aligned multimodal tool-oriented systems, with persistent limitations like hallucination and prompt sensitivity remaining unchanged.
citing papers explorer
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RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data
ATLAS traces RLVR data to 20 atomic sources, most datasets are variants, and DAPO++ curated with SCA improves RLVR performance while Q predicts training effectiveness.
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Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.
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From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences
The GPT family has shifted from scaled text predictors to aligned multimodal tool-oriented systems, with persistent limitations like hallucination and prompt sensitivity remaining unchanged.