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About this paper
Pengfei Gao, Chao Peng
ByteDance researchers empirically studied turn-control strategies for LLM-powered coding agents, demonstrating that dynamic turn allocation can achieve up to 68% cost reduction while maintaining or improving task performance on software engineering benchmarks.
Key insights
- Moderate pressure from turn constraints can induce more focused, expert-like problem-solving behaviors in agents, sometimes even improving performance by preventing chaotic or wasteful exploration.
- Dynamic turn allocation, which starts with a conservative budget and grants extensions only when needed, effectively balances high efficacy with cost management by preventing over-provisioning.
- No single LLM uniformly excels across effectiveness, turn efficiency, and economic cost; model selection should align with the primary objective (e.g., Claude 4 Sonnet for maximum success, GPT 4.1 for cost-efficiency).
Summary by alphaXiv.
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