It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs

arXiv:2609.37891 · cs.CL, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: Accepted at NeurIPS 2026. 35 pages, 9 figures. Dataset: https://huggingface.co/datasets/PleIAs/SYNTH

Code: https://github.com/xjdr-alt/entropix

Project page: https://physics.allen-zhu.com

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 40× in this paper · explained in 2 episodes
• Benchmark — 22× in this paper · explained in 2 episodes
• Reasoning Traces — 18× in this paper · explained in 3 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Ablation — 8× in this paper · explained in 1 episode
• Large Language Model — 8× in this paper
• Abstention — 6× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• RAG — 5× in this paper · explained in 3 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Scaling Laws — 4× in this paper · explained in 4 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• LLM-as-a-Judge — 3× in this paper · explained in 3 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Institutional Review Board — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Data Pruning — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Phi-4 Technical Report
• Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
• LFM2 Technical Report
• The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• Extracting memorized pieces of (copyrighted) books from open-weight language models
• BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling
• OpenThoughts: Data Recipes for Reasoning Models
• Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
• propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale
• Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls
• Data-efficient pre-training by scaling synthetic megadocs
• Kimi K2.5: Visual Agentic Intelligence
• Kimi K2: Open Agentic Intelligence
• WebSailor: Navigating Super-human Reasoning for Web Agent
• Combating Data Laundering in LLM Training
• Textbooks Are All You Need II: phi-1.5 technical report
• Learning Facts at Scale with Active Reading
• Best Practices and Lessons Learned on Synthetic Data

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