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Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Reinforcement learning for LLM-based explainable TCM prescription recommendation with implicit preferences from small

Xinyu Wang^1,2,3, Xiaohe Sun⁴, Lei Yang¹

¹School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, China.

Chinese Medicine

|November 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-stage framework for Traditional Chinese Medicine (TCM) prescription recommendations, enhancing accuracy and interpretability. The approach leverages knowledge distillation and reinforcement learning for improved clinical decision support.

Keywords:

BART Direct preference optimization Implicit preference Knowledge distillation TCM prescription recommendation

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Last Updated: Jan 11, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence
Computational Medicine
Traditional Chinese Medicine

Background:

Traditional Chinese Medicine (TCM) prescription recommendations require enhanced interpretability and accuracy.
Existing intelligent decision-support systems often lack transparency in their recommendations.

Purpose of the Study:

To develop a two-stage training framework for improving TCM prescription recommendation interpretability and accuracy.
To integrate knowledge distillation and implicit preference-driven reinforcement learning into a compact model.

Main Methods:

Utilized GPT-4o to parse TCM clinical records for distillation samples.
Employed Low-Rank Adaptation (LoRA) for fine-tuning the Qwen2.5-7B model to generate explainable outputs.
Trained a lightweight BART model and used Direct Preference Optimization (DPO) for reinforcement tuning.

Main Results:

Achieved P@30 of 35.62% and F1@30 of 37.36%, outperforming baselines.
Knowledge distillation improved generalization and explainability.
Reinforcement learning further enhanced F1@30 by 2.01%.

Conclusions:

The proposed approach enhances the quality and transparency of TCM prescription recommendations.
Offers a strategy for building trustworthy and clinically applicable intelligent TCM decision-support systems.