FiRE: Scaling and Aligning Financial Foundation Models

We introduce FiRE, a financial foundation model that combines generative pretraining with reward-guided post-training. Generative pretraining offers supervision directly from historical market trajectories, but scaling this paradigm exposes two limitations: high-capacity token spaces make categorical next-token prediction increasingly expensive, while sequence-modeling objectives are only indirectly aligned with the metrics that matter in downstream quantitative tasks. FiRE addresses the first challenge by encoding each candlestick bar as a 256-bit binary token, defining an implicit $2^{256}$ code space, and modeling next-token generation with conditional flow matching. This replaces vocabulary-sized categorical prediction with joint generation in the binary token space, enabling representation capacity to scale efficiently. For task-specific alignment, we introduce ReFLO (Rejected-Future Flow Optimization), which uses verifiable financial rewards to select low-scoring futures and constructs repulsive token-level regression targets, without differentiating through rewards or evaluating policy likelihoods. Extensive experiments demonstrate FiRE's superior performance across a range of quantitative finance tasks. FiRE improves return forecasting IC by 44% over the strongest baseline in the zero-shot setting, with the gain increasing to 51% after ReFLO post-training. It also achieves a 33% higher discriminative score than baselines for synthetic candlestick generation and delivers higher risk-adjusted returns in investment simulations.