The results show that targeted RL fine-tuning on high-quality, task-specific data can close — and reverse — a 231-billion-parameter gap in model size, at a training cost under $500, on a real financial reasoning benchmark.
Treat eval score gains as a diagnostic signal rather than a leaderboard goal — Khan's three-zone failure-analysis framework gives AI/coding practitioners a concrete method for extracting actionable improvements from broken benchmarks without overfitting to them.