LLM Agents Advance Complex Decision-Making Performance
Shangheng Du et al. (v2 submitted Feb 24, 2026) publish a comprehensive survey of optimization approaches for LLM-based agents, reviewing parameter-driven and parameter-free methods. The paper analyzes fine-tuning, reinforcement learning, hybrid strategies, prompt engineering, datasets, benchmarks, and applications, and identifies challenges like long-term planning and dynamic interactions. The authors provide a reference repository to support research and practical evaluation.
Key Points
- 1Categorizes optimization methods into parameter-driven (fine-tuning, RL, hybrid) and parameter-free (prompts, retrieval).
- 2Highlights need for agent-specific objectives like long-term planning and dynamic environment interaction for effective optimization.
- 3Suggests practitioners focus on trajectory construction, reward design, and benchmarks for robust agent evaluation.
Scoring Rationale
Comprehensive survey consolidates LLM-agent optimization methods, but offers limited novel algorithms beyond systematic synthesis and mainly compiles prior work.
Sources
Public references used for this report.
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