Cortex uses an Ontological Corpus Graph to structure web-scale corpora, creating a refined 24.14B-token corpus and a new benchmark validated on eight LLMs.
Source2synth: Synthetic data generation and curation grounded in real data sources
4 Pith papers cite this work. Polarity classification is still indexing.
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An agentic weak–strong Self-Instruct loop, optionally meta-optimized, produces synthetic data that trains small models better than standard CoT Self-Instruct across three domains.
Parallel inference rollouts aggregated into pseudo-references enable reference-free RL supervision that matches expert-annotated performance on health tasks while using 9x less test-time compute.
Context-instrumental data distillation allows a 1.5B SLM to generate valid Kubernetes manifests at 91.5% pass@1 rate, with strict output formatting proving more impactful than additional training data.
citing papers explorer
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CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
Cortex uses an Ontological Corpus Graph to structure web-scale corpora, creating a refined 24.14B-token corpus and a new benchmark validated on eight LLMs.
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Autodata: An agentic data scientist to create high quality synthetic data
An agentic weak–strong Self-Instruct loop, optionally meta-optimized, produces synthetic data that trains small models better than standard CoT Self-Instruct across three domains.
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Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Parallel inference rollouts aggregated into pseudo-references enable reference-free RL supervision that matches expert-annotated performance on health tasks while using 9x less test-time compute.
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Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation
Context-instrumental data distillation allows a 1.5B SLM to generate valid Kubernetes manifests at 91.5% pass@1 rate, with strict output formatting proving more impactful than additional training data.