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LLMs for High-Frequency Decision-Making: Normalized Action Reward-Guided Consistency Policy Optimization

arXiv:2603.02680v1 Announce Type: new Abstract: While Large Language Models (LLMs) form the cornerstone of sequential decision-making agent development, they have inherent limitations in high-frequency decision tasks. Existing research mainly focuses on discrete embodied decision scenarios with low-frequency and significant semantic differences in state space (e.g., household planning). These methods suffer from limited performance in high-frequency decision-making tasks, since high-precision numerical state information in such tasks undergoes frequent updates with minimal fluctuations, and exhibiting policy misalignment between the learned sub-tasks and composite tasks. To address these issues, this paper proposes Normalized Action Reward guided Consistency Policy Optimization (NAR-CP). 1) Our method first acquires predefined dense rewards from environmental feedback of candidate actions via reward functions, then completes reward shaping through normalization, and theoretically veri

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Yang Zhao, Zihao Li, Zhiyu Jiang, Dandan Ma, Ganchao Liu, Wenzhe Zhao · · 1 min read · 2 views
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arXiv:2603.02680v1 Announce Type: new Abstract: While Large Language Models (LLMs) form the cornerstone of sequential decision-making agent development, they have inherent limitations in high-frequency decision tasks. Existing research mainly focuses on discrete embodied decision scenarios with low-frequency and significant semantic differences in state space (e.g., household planning). These methods suffer from limited performance in high-frequency decision-making tasks, since high-precision numerical state information in such tasks undergoes frequent updates with minimal fluctuations, and exhibiting policy misalignment between the learned sub-tasks and composite tasks. To address these issues, this paper proposes Normalized Action Reward guided Consistency Policy Optimization (NAR-CP). 1) Our method first acquires predefined dense rewards from environmental feedback of candidate actions via reward functions, then completes reward shaping through normalization, and theoretically verifies action reward normalization does not impair optimal policy. 2) To reduce policy misalignment in composite tasks, we use LLMs to infer sub-observation candidate actions and generate joint policies, with consistency loss ensuring precise alignment between global semantic policies and sub-semantic policies. Experiments on UAV pursuit, a typical high-frequency task, show our method delivers superior performance on independent and composite tasks with excellent generalization to unseen tasks.

Executive Summary

This paper addresses the limitations of Large Language Models (LLMs) in high-frequency decision-making tasks. Existing methods are primarily designed for low-frequency decision scenarios with significant semantic differences in state space. The proposed Normalized Action Reward-guided Consistency Policy Optimization (NAR-CP) method acquires predefined dense rewards from environmental feedback and normalizes action rewards to ensure policy alignment. The approach uses LLMs to infer sub-observation candidate actions and generate joint policies with consistency loss. The method demonstrates superior performance in high-frequency tasks, such as UAV pursuit, and excellent generalization to unseen tasks. The paper contributes to the development of more efficient and effective decision-making agents for high-frequency tasks.

Key Points

  • ▸ Existing research focuses on discrete embodied decision scenarios with low-frequency and significant semantic differences in state space.
  • ▸ The proposed NAR-CP method addresses limitations in high-frequency decision tasks by normalizing action rewards and ensuring policy alignment.
  • ▸ The approach uses LLMs to infer sub-observation candidate actions and generate joint policies with consistency loss.

Merits

Strength in handling high-frequency decision tasks

The NAR-CP method demonstrates superior performance in high-frequency tasks, such as UAV pursuit, and excellent generalization to unseen tasks.

Demerits

Assumes predefined dense rewards

The method assumes the availability of predefined dense rewards from environmental feedback, which may not be feasible in all scenarios.

Expert Commentary

The paper makes a significant contribution to the field of decision-making by addressing the limitations of LLMs in high-frequency tasks. The proposed NAR-CP method demonstrates superior performance and excellent generalization to unseen tasks. However, the assumption of predefined dense rewards may limit the method's applicability in certain scenarios. The paper's findings have practical implications for the development and deployment of autonomous systems, and its policy implications highlight the need for more efficient and effective decision-making agents. The proposed method can be seen as a step towards more advanced decision-making agents, which can potentially be applied to various high-frequency decision tasks.

Recommendations

  • ✓ Future research should investigate methods to acquire predefined dense rewards in scenarios where they are not readily available.
  • ✓ The proposed method can be combined with other decision-making approaches to enhance its performance and applicability in various scenarios.

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