FinEvolveBench: A Benchmark for Self-Evolving Agents on Low-Repetition Tasks with Implicit Rewards

Abstract

Large language model (LLM) agents increasingly rely on external experience to continually adapt to changing environments without modifying their underlying models. Recent experience mechanisms have demonstrated promising results across diverse tasks. However, their effectiveness is typically evaluated within individual benchmark settings, and how experience mechanisms generalize across different scenarios remains insufficiently explored. In this work, we present a scenario-oriented analysis of experience mechanisms for LLM agents. We characterize existing evaluation scenarios along four dimensions: outcome observability, credit assignment complexity, environmental dynamics, and experience reusability. Our analysis shows that existing benchmarks often evaluate experience mechanisms under scenarios where at least one dimension is comparatively favorable, leaving more challenging combinations of scenario properties underexplored. To address this gap, we introduce \textsc{FinEvolveBench}, a reproducible benchmark built on a chronological stream of rich financial news and market data that enables systematic evaluation of experience-based self-evolution under challenging experience regimes characterized by noisy feedback, ambiguous credit assignment, environmental non-stationarity, and limited experience reusability. Experiments show that existing approaches exhibit substantially reduced or inconsistent gains in this setting, highlighting the scenario-dependent nature of experience mechanisms and the challenge of maintaining valid experience under changing environments.