arXiv cs.CLPaper
Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning
The mechanics are reasonable: mine reasoning traces, distill instructions, generate synthetic pairs from textbooks, deduplicate, classify, then fine-tune or use RL. It's domain-specific work on a real problem, but the techniques are standard. If you're building a financial LLM this gives you a playbook. Everyone else sees a competent execution of known methods.