通过自提示与状态追踪提升大模型代理的长期决策能力
StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
- 引入自提示机制强化每步任务目标，结合状态链跟踪进展
- 在Alfworld、Textcraft等数据集上性能超越ReAct超10%
- 无需额外训练即可替换现有代理，适合长序列任务研究者
大语言模型（LLMs）正被广泛用于自主代理，处理从机器人到网页导航的任务。其表现依赖于底层基础代理。现有方法在长上下文推理和目标一致性方面存在瓶颈。我们提出StateAct，一种新颖且高效的基线代理，通过（1）自提示机制，在每一步强化任务目标；（2）状态链（chain-of-states），扩展链式思维以追踪时间维度的状态信息。StateAct在Alfworld上性能优于ReAct超过10%，在Textcraft上提升30%，在Webshop上提升7%，覆盖多个前沿大模型。此外，作为ReAct的即插即用替代品，结合测试时缩放等高级方法，可在Textcraft上再获得12%增益。该方法无需额外训练或检索，显著提升效率与长程推理能力，为大模型代理提供可扩展基础。代码已开源：https://github.com/ai-nikolai/stateact。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used as autonomous agents, tackling tasks from robotics to web navigation. Their performance depends on the underlying base agent. Existing methods, however, struggle with long-context reasoning and goal adherence. We introduce StateAct, a novel and efficient base agent that enhances decision-making through (1) self-prompting, which reinforces task goals at every step, and (2) chain-of-states, an extension of chain-of-thought that tracks state information over time. StateAct outperforms ReAct, the previous best base agent, by over 10% on Alfworld, 30% on Textcraft, and 7% on Webshop across multiple frontier LLMs. We also demonstrate that StateAct can be used as a drop-in replacement for ReAct with advanced LLM agent methods such as test-time scaling, yielding an additional 12% gain on Textcraft. By improving efficiency and long-range reasoning without requiring additional training or retrieval, StateAct provides a scalable foundation for LLM agents. We open source our code to support further research at https://github.com/ai-nikolai/stateact .
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