mlsanityData to 7 Oct 2026

Resource-Efficient Fine-Tuning of LLaMA-3.2-3B for Medical Chain-of-Thought Reasoning

arXiv 2510.05003 ↗First posted 6 Oct 2025cs.CL cs.AIcited by 0

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Large Language Models (LLMs) such as GPT-4 and LLaMA have demonstrated remarkable reasoning abilities but require significant computational resources for fine-tuning. This paper presents a resource-efficient fine-tuning approach for LLaMA-3.2-3B to enhance medical chain-of-thought reasoning while operating under constrained GPU and memory settings. Using parameter-efficient tuning techniques such as LoRA and QLoRA, we adapt the base model on publicly available medical reasoning datasets. The model achieves improved reasoning coherence and factual accuracy while reducing memory usage by up to 60% compared to standard full fine-tuning. Experimental evaluation demonstrates that lightweight adaptations can retain strong reasoning capability in medical question-answering tasks. This work highlights practical strategies for deploying LLMs in low-resource research environments and provides insights into balancing efficiency and domain specialization for medical AI systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 17 of its 27 references that are in the map. First posted on arXiv 6 Oct 2025.

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17 of 27 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  7. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  11. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  12. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
5 more
  1. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  3. 2019Publicly Available Clinical BERT Embeddingscited by 995
  4. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  5. 2016Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Recordscited by 20

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  2. 2026Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
  3. 2026To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
  4. 2025FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training
  5. 2025MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine
  6. 2025Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs
  7. 2025m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
  8. 2025Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications