AI RESEARCH
SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents
arXiv CS.LG
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ArXi:2605.06822v1 Announce Type: new Large language models (LLMs) are increasingly deployed for autonomous financial trading, a domain requiring continuous adaptation to noisy, non-stationary markets. Existing self-improving agents typically address this through unbounded free-form prompt optimization. However, in low signal-to-noise environments with delayed scalar rewards (P\&L), this unstructured approach exacerbates the fundamental credit assignment problem: optimizers cannot reliably distinguish systematic logic flaws from stochastic market variance, inevitably leading to policy drift.