ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning
Tik Yu Yim, Wenting Tan, Sum Yee Chan, Tak-Wah Lam, Siu Ming Yiu

TL;DR
ASDA introduces a novel, training-free framework that automatically generates structured skills for financial reasoning, significantly improving LLM performance without model fine-tuning.
Contribution
It proposes a new error-corrective learning approach to create human-readable skill artifacts for domain adaptation without modifying model weights.
Findings
Achieves up to +17.33% improvement on arithmetic reasoning
Outperforms existing training-free methods on FAMMA benchmark
Produces human-readable, version-controlled skill artifacts
Abstract
Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have emerged, yet our experiments show that leading methods (GEPA and ACE) achieve only marginal gains on the FAMMA financial reasoning benchmark, exposing the limits of unstructured text optimization for complex, multi-step domain reasoning. We introduce Automated Skill Distillation and Adaptation (ASDA), a framework that automatically generates structured skill artifacts through iterative error-corrective learning without modifying model weights. A teacher model analyzes a student model's failures on financial reasoning tasks, clusters errors by subfield and error type, and synthesizes skill files containing reasoning procedures, code templates, and worked examples, which are dynamically injected during…
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Taxonomy
TopicsTopic Modeling · Text Readability and Simplification · Multimodal Machine Learning Applications
