LLM Agents Unveiled: A Comprehensive Survey of Optimization Strategies for Large Language Model-Based Intelligent Agents

The rise of large language models (LLMs) like GPT-4 and PaLM has sparked a paradigm shift in artificial intelligence, enabling systems to perform complex tasks traditionally reserved for humans. However, LLMs were originally trained for text prediction, not sequential decision-making or real-world interaction. To bridge this gap, researchers are increasingly focusing on optimizing LLMs into autonomous agents capable of planning, reasoning, and learning in dynamic environments. A recent survey by East China Normal University and Donghua University provides the first systematic analysis of optimization strategies for LLM-based agents, categorizing methods into parameter-driven and parameter-free approaches.

Parameter-Driven Optimization: Shaping Model Behavior Through Training

Parameter-driven methods modify the LLM’s weights to align its behavior with specific agent tasks. These techniques fall into three main categories:

1. Supervised Fine-Tuning (SFT)

SFT trains LLMs on task-specific trajectories to improve reasoning and planning. Key challenges include:

2. Reinforcement Learning (RL)

RL optimizes agents through trial-and-error interactions. Notable approaches include:

3. Hybrid Strategies

Combining SFT and RL offers complementary benefits:

Parameter-Free Optimization: Enhancing Performance Without Retraining

Parameter-free methods improve agent behavior without altering model weights, making them lightweight and deployable:

1. Memory-Augmented Reasoning

2. Prompt Engineering

3. Tool Integration

4. Multi-Agent Collaboration

Datasets and Applications

The survey highlights key datasets for training and evaluating agents:

Applications span industries:

Challenges and Future Directions

Future innovations may include self-supervised RL for sparse data environments, adversarial validation to improve robustness, and neuro-symbolic integration for logical reasoning.