Heng Huang

A robust RL framework for quantitative trading with introducing novel elliptic uncertainty sets to model the market impact.

CSCR embeds both prompts and LLMs into a shared space using fast logit or perplexity fingerprints. A cost‑banded InfoNCE loss trains the space to balance quality against cost. It generalizes to unseen models and out‑of‑distribution prompts.

We propose a class of unbiased zeroth-order gradient estimator.