#4858 Text Book to Model
#4858 Text Book to Model #4858 How best to train an AI model with text book Classical Dynamics of Particles and Systems Training an LLM effectively on Classical Dynamics of Particles and Systems (Marion & Thornton) requires avoiding raw text continuous pre-training (CPT), which causes mathematical token fragmentation, hallucinated coordinate transformations, and sign errors in Euler-Lagrange derivations. The gold standard for scientific textbooks is an Extraction-to-Synthesis pipeline paired with Reinforcement Learning with Verifiable Rewards (RLVR) or SymPy-validated Supervised Fine-Tuning (SFT) . Phase 1: High-Fidelity Extraction & AST Canonicalization Raw OCR or basic PDF parsing corrupts superscripts, tensor indices, and generalized coordinate dots ( dot{q}_i , ddot{theta} ). Document Decomposition: Ingest chapters using layout-aware parsers (e.g., Docling or PyMuPDF/ fitz ) to segment text, theorem callouts, worked examples, and end-of-chapter problems. Separate narr...