Academic

RF-Agent: Automated Reward Function Design via Language Agent Tree Search

arXiv:2602.23876v1 Announce Type: new Abstract: Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward functions. These methods typically rely on training results as feedback, iteratively generating new reward functions with greedy or evolutionary algorithms. However, they suffer from poor utilization of historical feedback and inefficient search, resulting in limited improvements in complex control tasks. To address this challenge, we propose RF-Agent, a framework that treats LLMs as language agents and frames reward function design as a sequential decision-making process, enhancing optimization through better contextual reasoning. RF-Agent integrates Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi-stage contextual reasoning ability of LLMs. This a

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Ning Gao, Xiuhui Zhang, Xingyu Jiang, Mukang You, Mohan Zhang, Yue Deng · · 1 min read · 2 views
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arXiv:2602.23876v1 Announce Type: new Abstract: Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Models (LLMs) with task information to generate dense reward functions. These methods typically rely on training results as feedback, iteratively generating new reward functions with greedy or evolutionary algorithms. However, they suffer from poor utilization of historical feedback and inefficient search, resulting in limited improvements in complex control tasks. To address this challenge, we propose RF-Agent, a framework that treats LLMs as language agents and frames reward function design as a sequential decision-making process, enhancing optimization through better contextual reasoning. RF-Agent integrates Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi-stage contextual reasoning ability of LLMs. This approach better utilizes historical information and improves search efficiency to identify promising reward functions. Outstanding experimental results in 17 diverse low-level control tasks demonstrate the effectiveness of our method. The source code is available at https://github.com/deng-ai-lab/RF-Agent.

Executive Summary

The article proposes a novel framework, RF-Agent, for designing efficient reward functions for low-level control tasks. RF-Agent integrates Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS) to manage the reward design and optimization process, leveraging the multi-stage contextual reasoning ability of LLMs. This approach improves search efficiency and better utilizes historical information, leading to significant improvements in complex control tasks. The authors demonstrate the effectiveness of RF-Agent through outstanding experimental results in 17 diverse low-level control tasks. The proposed framework has the potential to reduce reliance on expert experience and improve the efficiency of reward function design. However, the article does not provide a comprehensive evaluation of the limitations and challenges of the proposed approach, which may hinder its widespread adoption.

Key Points

  • ▸ RF-Agent is a novel framework for designing efficient reward functions using LLMs and MCTS.
  • ▸ The framework leverages the multi-stage contextual reasoning ability of LLMs to improve search efficiency.
  • ▸ RF-Agent demonstrates significant improvements in complex control tasks through outstanding experimental results.

Merits

Strength

The proposed framework has the potential to reduce reliance on expert experience and improve the efficiency of reward function design.

Improved Search Efficiency

RF-Agent integrates MCTS to manage the reward design and optimization process, leading to significant improvements in search efficiency.

Better Utilization of Historical Information

The framework leverages the multi-stage contextual reasoning ability of LLMs to better utilize historical information and improve search efficiency.

Demerits

Limitation

The article does not provide a comprehensive evaluation of the limitations and challenges of the proposed approach, which may hinder its widespread adoption.

Dependence on LLMs

The proposed framework relies heavily on the performance of LLMs, which may be subject to biases and errors.

Scalability

The framework may not be scalable to handle complex tasks with a large number of variables and constraints.

Expert Commentary

The article proposes a novel framework for designing efficient reward functions using LLMs and MCTS. The framework has the potential to reduce reliance on expert experience and improve the efficiency of reward function design. However, the article does not provide a comprehensive evaluation of the limitations and challenges of the proposed approach, which may hinder its widespread adoption. The proposed framework is related to the ongoing research in reward function design, which aims to reduce reliance on expert experience and improve the efficiency of reward function design. The framework relies heavily on the performance of LLMs, which may be subject to biases and errors. The article highlights the importance of developing more efficient reward function design methods, which may have significant implications for the development of more intelligent and autonomous systems.

Recommendations

  • ✓ Further evaluation of the limitations and challenges of the proposed approach is necessary to ensure its widespread adoption.
  • ✓ The framework should be tested on more complex tasks with a large number of variables and constraints to evaluate its scalability.
  • ✓ The performance of LLMs should be evaluated in more detail to ensure that they are reliable and unbiased.

Sources

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