LLM Training Data (Physics)

Product Overview

This product is a Physics-domain LLM Training Data Package covering 6 core physics disciplines, containing high-quality physics problems, reasoning chains, and solution processes ranging from high school to research level. Directly applicable for LLM fine-tuning (SFT), reinforcement learning (RLHF/GRPO), preference alignment training, and RAG retrieval augmentation.

• Total Data: ~12M+ high-quality physics QA pairs and reasoning chains
• Token Scale: ~8.5 billion tokens (including complete reasoning processes)
• Difficulty Range: High school competition → Undergraduate → Graduate/PhD qualifying → Research-frontier level
• Data Format: JSONL / Parquet (with structured field annotations)
• Quality Standards: Multi-round deduplication (exact + MinHash + semantic), benchmark contamination detection, manual spot-check verification

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Covered Disciplines

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Data Quality & Features

• Complete Chain-of-Thought: Each problem includes detailed analysis → modeling → formula derivation → calculation → conclusion reasoning process
• Multi-Solution Annotations: ~30% of hard problems include 2-3 alternative solution paths for flexible physical reasoning
• Formula Standardization: All mathematical formulas in standardized LaTeX format
• Difficulty Grading: 1-10 difficulty levels, from high school (1-3) to PhD (7-9) to frontier research (10)
• Benchmark Decontamination: Automatically filtered against UGPhysics, TPBench, PHYSICS Benchmark to prevent data leakage
• Multi-Stage Deduplication: Exact → MinHash+LSH fuzzy → GTE vector semantic deduplication pipeline

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Compatible Evaluation Benchmarks

• UGPhysics: 5,520 undergraduate physics problems across 13 sub-disciplines
• TPBench: Theoretical physics frontier reasoning (high-energy physics, cosmology)
• PHYSICS Benchmark (Yale NLP): 1,297 PhD qualifying exam problems across 6 disciplines
• SciEval / MMLU-Physics: Physics subsets of general science evaluations

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Use Cases

• Physics-Specialized LLM Fine-Tuning: Inject professional physics reasoning into general-purpose LLMs
• Research AI Assistants: Accelerate theoretical derivation, literature analysis, experimental design
• EdTech: Intelligent physics tutoring systems, adaptive problem generation
• STEM Benchmark Training: Improve physics scores on MMLU, ARC, SciQ evaluations
• Physics Simulation & Engineering: CFD/materials mechanics simulation assistance, engineering calculation verification

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Delivery Information

• Data Files: JSONL / Parquet (as agreed), with README documentation and field descriptions
• Delivery Time: Typically 3-7 business days
• Data Updates: Incremental update service available upon request
• Compliance: No personal privacy data included; for special compliance requirements, please communicate before ordering