Academic

Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation

arXiv:2602.18749v1 Announce Type: new Abstract: Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation methods fail to address an under-explored challenge in collaboration: bidirectional model learnability gap, where client-side SLMs cannot identify high-reward samples matching their learnability constraints for effective knowledge transfer from LLMs, while LLMs struggle to select samples contributing novel knowledge beyond their existing data. Furthermore, these collaboration frameworks face another key challenge: domain-agnostic reasoning transfer, where existing reasoning transfer methods fail to flexibly adapt to the local domain data, preventing SLMs from effectively acquiring step-by-step reasoning abilities within from general LLM. To address these challenges, we propose LaDa, a federated reasoning distillation framework with model learnability-aware data allo

W
Wei Guo, Siyuan Lu, Xiangdong Ran, Yiqi Tong, Yikun Ban, Zelong Xu, Jing Fan, Zixuan Huang, Xiao Zhang, Zhaojun Hu, Fuzhen Zhuang · · 1 min read · 2 views
#cs.AI

arXiv:2602.18749v1 Announce Type: new Abstract: Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation methods fail to address an under-explored challenge in collaboration: bidirectional model learnability gap, where client-side SLMs cannot identify high-reward samples matching their learnability constraints for effective knowledge transfer from LLMs, while LLMs struggle to select samples contributing novel knowledge beyond their existing data. Furthermore, these collaboration frameworks face another key challenge: domain-agnostic reasoning transfer, where existing reasoning transfer methods fail to flexibly adapt to the local domain data, preventing SLMs from effectively acquiring step-by-step reasoning abilities within from general LLM. To address these challenges, we propose LaDa, a federated reasoning distillation framework with model learnability-aware data allocation. It introduces a model learnability-aware data filter that adaptively allocates high-reward samples based on the learnability gap between each SLM and LLM pair, effectively facilitating bidirectional knowledge transfer. We further design a domain adaptive reasoning distillation method that aligns joint probabilities of reasoning paths on filtered high-reward samples through contrastive distillation learning between SLM and LLM, enabling SLM to capture underlying reasoning patterns under local data distribution. LaDa operates as a plug-in module for existing collaboration frameworks, adapting knowledge transfer based on model learnability gaps.

Executive Summary

This article proposes LaDa, a federated reasoning distillation framework that addresses the under-explored challenge of bidirectional model learnability gap in large language model (LLM) and small language model (SLM) collaboration. LaDa introduces a model learnability-aware data filter to adaptively allocate high-reward samples and a domain adaptive reasoning distillation method for effective knowledge transfer. The framework enables SLMs to capture underlying reasoning patterns under local data distribution, facilitating domain-agnostic reasoning transfer. LaDa operates as a plug-in module for existing collaboration frameworks, adapting knowledge transfer based on model learnability gaps. The framework has the potential to improve the efficiency and effectiveness of LLM-SLM collaboration, enabling more accurate and flexible reasoning abilities.

Key Points

  • ▸ LaDa addresses the bidirectional model learnability gap in LLM-SLM collaboration
  • ▸ LaDa introduces a model learnability-aware data filter for adaptive sample allocation
  • ▸ LaDa employs a domain adaptive reasoning distillation method for effective knowledge transfer

Merits

Strength in addressing the under-explored challenge of bidirectional model learnability gap

LaDa's innovative approach to addressing the bidirectional model learnability gap is a significant merit, as existing methods have failed to address this critical challenge in LLM-SLM collaboration.

Flexibility in adapting to local domain data

LaDa's domain adaptive reasoning distillation method enables SLMs to effectively acquire step-by-step reasoning abilities within their local domain data, making it a valuable feature for practical applications.

Demerits

Technical complexity of implementation

LaDa's framework may require significant technical expertise to implement, which could be a limitation for researchers and practitioners without extensive experience in AI and machine learning.

Potential scalability issues

LaDa's framework may not be scalable to handle large datasets or complex models, which could limit its practical applications.

Expert Commentary

LaDa is a significant contribution to the field of LLM-SLM collaboration, as it addresses a critical challenge in the field. However, the technical complexity of implementation and potential scalability issues may limit its practical applications. Further research is needed to fully explore the implications of LaDa and to address these limitations. Additionally, the framework's potential implications for policy-making in areas such as artificial intelligence regulation warrant further investigation.

Recommendations

  • ✓ Researchers and practitioners should carefully evaluate LaDa's framework and its potential applications in practical scenarios.
  • ✓ Further research is needed to explore the implications of LaDa and to address the technical complexity of implementation and potential scalability issues.

Sources

Related Articles