ASDA: Automated Skill Distillation and Adaptation for Financial Reasoning
arXiv:2603.16112v1 Announce Type: new 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 inference. Evaluated on FAMMA, ASDA achieves up to +17.33
arXiv:2603.16112v1 Announce Type: new 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 inference. Evaluated on FAMMA, ASDA achieves up to +17.33% improvement on arithmetic reasoning and +5.95% on non-arithmetic reasoning, substantially outperforming all training-free baselines. The resulting skill artifacts are human-readable, version-controlled, and compatible with the Agent Skills open standard, offering any organization with a labeled domain dataset a practical and auditable path to domain adaptation without weight access or retraining.
Executive Summary
This article presents ASDA, a framework for automating skill distillation and adaptation in large language models (LLMs) for financial reasoning tasks. By leveraging iterative error-corrective learning and structured skill artifacts, ASDA achieves significant improvements over existing methods, with up to +17.33% improvement on arithmetic reasoning and +5.95% on non-arithmetic reasoning. The framework generates human-readable skill artifacts that are version-controlled and compatible with the Agent Skills open standard, offering a practical and auditable path to domain adaptation without weight access or retraining. The study demonstrates the effectiveness of ASDA on the FAMMA financial reasoning benchmark, providing a valuable contribution to the field of LLM adaptation and finetuning.
Key Points
- ▸ ASDA is a framework for automating skill distillation and adaptation in LLMs for financial reasoning tasks
- ▸ ASDA achieves significant improvements over existing methods on the FAMMA financial reasoning benchmark
- ▸ The framework generates human-readable skill artifacts that are version-controlled and compatible with the Agent Skills open standard
Merits
Improved Performance
ASDA achieves significant improvements over existing methods on the FAMMA financial reasoning benchmark, demonstrating its effectiveness in automating skill distillation and adaptation.
Demerits
Limited Domain Scope
The study focuses on financial reasoning tasks, and it is unclear whether ASDA can be applied to other domains with similar success.
Expert Commentary
The ASDA framework represents a significant advancement in the field of LLM adaptation and fine-tuning. By leveraging structured skill artifacts and iterative error-corrective learning, ASDA demonstrates a more efficient and effective approach to automating skill distillation and adaptation. The study's findings have important implications for organizations seeking to leverage LLMs for financial reasoning tasks, particularly those with limited resources or expertise. However, further research is needed to explore the applicability of ASDA to other domains and to address potential limitations related to its scalability and interpretability.
Recommendations
- ✓ Future research should investigate the generalizability of ASDA to other domains and explore its potential applications in areas such as healthcare and education.
- ✓ Developers and policymakers should prioritize the development of regulatory frameworks that accommodate the use of ASDA and similar frameworks for LLM adaptation and fine-tuning.
