⚡ electrical engineering

ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)

The paper proposes ERFSL, an efficient framework that leverages large language models to automatically generate, critique, and iteratively optimize reward function components and weights for custom multi-objective learning tasks, achieving rapid convergence to user requirements with minimal feedback iterations.

Original authors: Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang

Published 2026-05-20

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CC BY 4.0

Original authors: Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to teach a robot to play a complex video game. You want the robot to do three things at once: avoid crashing, save battery power, and collect as many items as possible. The problem is, telling a robot how to do this is incredibly hard. You have to write a "scorecard" (called a reward function) that tells the robot exactly how many points to give for every action. If you get the math wrong, the robot might crash constantly or ignore the items entirely.

This paper introduces ERFSL, a smart assistant that uses a Large Language Model (like a super-smart AI chatbot) to automatically write and tune this scorecard for you.

Here is how ERFSL works, broken down into simple steps:

1. The Translator (Environment Description)

First, you tell the AI what you want in plain English (e.g., "Don't crash," "Save energy"). The AI acts like a translator, turning your vague wishes into a specific, numbered checklist. It also checks your description to make sure it's clear, asking you to fill in missing details if the instructions are too fuzzy.

2. The Code Writer & The Editor (Reward Code Generation)

Next, the AI tries to write the actual computer code for the scorecard.

The Writer: It creates a small piece of code for each of your requirements.
The Editor (Reward Critic): Since the AI might make mistakes (like using a variable that doesn't exist), a "critic" checks the code. If the code is broken, the critic points out the error, and the AI fixes it immediately. The paper claims this is very efficient; usually, the AI gets the code right after just one round of feedback.

3. The Tuner (Reward Weight Search)

This is the hardest part. Imagine you have three knobs on a radio: one for "Crash Penalty," one for "Energy Penalty," and one for "Item Bonus."

If you turn the "Crash" knob too high, the robot is too scared to move.
If you turn it too low, it crashes constantly.
You need to find the perfect balance so the robot does everything well.

ERFSL uses a clever strategy to find these perfect settings:

The Initial Guess: It starts by testing 5 different combinations of knob settings to see what happens.
The Log Reader: It looks at a "training diary" (logs) that shows how the robot performed. Did it crash? Did it run out of battery?
The Genetic Tuner: Based on the diary, the AI acts like a genetic engineer. It takes the best-performing settings, mixes them up, and tries new combinations. It decides whether to turn a knob up, down, or just tweak it slightly.

Why is this special?

The paper highlights a few "superpowers" of this system:

It's Resilient: Even if you accidentally set one of the knobs to be 500 times too strong (a massive mistake), the system can find the right balance in just about 5 tries.
It Works with Smarter Models: It works well even with smaller, faster AI models (like GPT-4o mini), not just the biggest, most expensive ones.
It's Modular: Instead of trying to fix the whole scorecard at once, it breaks the problem into small pieces (writing the code, then tuning the weights), making it much less likely to get confused.

The Bottom Line

Think of ERFSL as a smart coach for your robot. Instead of you struggling to write complex math equations to teach the robot, you just tell the coach your goals. The coach writes the rules, checks them for errors, and then plays around with the settings until the robot performs perfectly. The researchers tested this on a simulation involving underwater robots (AUVs) and found that it could quickly generate a set of rules that balanced safety, energy, and performance better than previous methods.

Technical Summary of ERFSL: An Efficient Reward Function Searcher via Language Models

Problem Statement
Learning-based (LB) methods are effective for multi-objective tasks, yet designing complex reward functions remains a significant bottleneck due to ambiguous and varied user requirements. While Large Language Models (LLMs) have shown promise in aiding reward design for customized control and virtual game scenarios, they often struggle with intricate tasks, producing incorrect code or generating reward functions with imbalanced weights. Existing approaches that decompose tasks into sub-skills provide feedback but may not fully leverage the semantic understanding of LLMs for end-to-end reward optimization in custom environments.

Methodology: ERFSL
The authors propose ERFSL (Efficient Reward Function Searcher via Language Models), a framework that decomposes LLM-aided reward generation into two distinct phases: reward component design and reward weight assignment. The system operates under a textual context of explicit user requirements and clear feedback loops.

Environment Description & Meta-Prompting: The system accepts natural language descriptions, environment code/APIs, and user requirements. A meta-prompt is employed to enhance description quality, decomposing user requirements into specific numerical objectives (e.g., zero constraint violations) to reduce ambiguity and facilitate human modification.
Reward Code Generation (Stage 1):
- Component Design: The LLM generates a specific reward code component for each user requirement.
- Reward Critic: To address potential errors arising from a lack of prior environment knowledge, each component is tested separately. A "reward critic" analyzes the output and provides explicit feedback to correct code errors. This process typically requires only one feedback iteration per requirement.
- Handling Incompleteness: The LLM can fabricate necessary variables and prompt users to complete them, effectively managing incomplete environment descriptions.
Reward Weight Search (Stage 2):
- Weight Initializer (RWI): To ensure reward components are on a comparable scale, the system pre-calculates component values and designates $K=5$ groups of initial weights.
- Iterative Optimization: A training log analyzer summarizes training results. Based on this context, the LLM suggests $K=5$ weight adjustments.
- Search Strategy: Inspired by genetic algorithms, the searcher specifies a starting point and an adjustment direction (increase/decrease/fine-tune) to mutate weights. For multiple adjustments, the system performs crossovers between mutated input groups.
- Prompt Engineering: The process separates the generation of adjustment suggestions from the output of weight groups to shorten prompts and ensure precise execution.

Key Contributions

Decomposition Strategy: The paper introduces a white-box search approach that separates reward component generation from weight assignment, leveraging LLMs' semantic understanding under explicit feedback.
Efficient Correction Mechanism: The integration of a "reward critic" allows for the correction of code errors with minimal iterations (often just one per component), addressing a common failure mode in baseline methods.
Robust Weight Search: The framework employs a flexible step-size strategy and crossover mechanisms to navigate the Pareto set of reward functions, capable of recovering even when initial weights are significantly off (e.g., by a factor of 500).
Model Agnosticism: The approach is demonstrated to work adequately with smaller, cost-effective models like GPT-4o mini, provided the search process is decomposed to reduce context length.

Experimental Results
The authors evaluated ERFSL on a simulation-based benchmark task involving mobile units (AUVs) for data transfer, comparing it against a baseline named EUREKA-M (an adaptation of the Eureka framework).

Code Correction: The reward critic successfully corrected code errors with a single feedback iteration per component, whereas baselines like EUREKA-M struggled with hard-to-correct errors due to a lack of explicit, task-split feedback.
Weight Search Efficiency:
- With a standard setup, the Reward Weight Initializer (RWI) generated diverse solutions within the Pareto set.
- Even when the energy weight was initialized with a 500x deviation, ERFSL required an average of only 5.2 iterations to meet user requirements.
- Removing the Training Log Analyzer (TLA) increased the iteration count, highlighting the importance of context analysis.
- The baseline EUREKA-M, which used small, random step sizes, required substantially more iterations to converge.
Performance with Smaller Models: When using GPT-4o mini, the reward code generation and weight search performance remained adequate, surpassing EUREKA-M, though the step-size selection was less flexible than with the larger GPT-4o model.

Significance and Claims
The paper claims that ERFSL offers a practical and efficient solution for designing reward functions in custom, multi-objective environments. By decomposing the task and utilizing explicit feedback loops, the method mitigates the risks of code fabrication and weight imbalance often seen in LLM-aided design. The authors emphasize that the system does not require advanced reasoning capabilities from the LLM, making it accessible even with smaller models like GPT-4o mini. The work aims to demonstrate that LLMs can serve as effective "searchers" for reward functions when guided by structured prompts and iterative feedback, rather than relying solely on their generative capabilities.