Technical deep-dives on training data, RL environments, model training, and building AI systems that can operate in financial markets.
Comprehensive comparison of financial AI training data sources for researchers and practitioners.
A financial model trained once becomes stale. Markets evolve and static datasets create brittle systems.
Raw sentiment scores fail. Effective social data requires engagement weighting and noise filtering.
Getting AI to reliably execute financial transactions requires more than good models.
Financial firms have used ML for decades. LLM training is fundamentally different.
Financial AI capabilities evolve in stages. Each stage requires different training data.
Frontier labs reportedly pay over $100/hr for expert feedback. That doesn't scale. Here's what financial AI actually needs.
Static datasets can't train decision-making. Replayable environments let AI explore alternatives.
Every trade yields one outcome, but multiple learning opportunities. Counterfactual analysis extracts maximum signal.
Outcomes teach models what happened. Reasoning traces teach models how to think.
What goes into a complete training episode for financial AI? A technical deep-dive into the data structure.
Post-training transforms base LLMs into useful assistants. Learn how RLHF, DPO, and domain-specific training work.
Financial AI is bottlenecked by a shortage of quality training data. Market data alone doesn't teach models to make decisions.