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.
Scoring Rationale
Comprehensive survey consolidates LLM-agent optimization methods, but offers limited novel algorithms beyond systematic synthesis and mainly compiles prior work.
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