Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

The Chain of Uncertain Rewards (CoUR) is a novel framework that integrates large language models (LLMs) to enhance reward function design and evaluation in reinforcement learning (RL) environments. CoUR addresses the inefficiencies and inconsistencies of traditional methods by introducing Code Uncertainty Quantification (CUQ) and Bayesian Decoupling Optimization (BDO). CUQ uses textual and semantic similarity analyses to identify and refine ambiguous reward components, reducing redundancy. BDO decouples reward terms and optimizes their hyperparameters independently using Bayesian optimization, leading to more efficient exploration and robust solutions. Evaluated across nine IsaacGym environments and 20 Bidexterous Manipulation benchmark tasks, CoUR achieved superior performance, with a normalized score of 5.62 on IsaacGym and a 65.63% success rate on Bidexterous Manipulation, significantly outperforming baselines like Text2Reward and human-engineered rewards while lowering evaluation costs.

Key takeaway

For research scientists developing reinforcement learning agents, CoUR offers a structured approach to overcome the labor-intensive and error-prone nature of reward function design. You should consider integrating uncertainty quantification and decoupled Bayesian optimization into your LLM-driven reward generation workflows to achieve higher performance and significantly reduce computational costs compared to traditional or monolithic LLM-only methods.

Key insights

CoUR uses LLMs, uncertainty quantification, and decoupled Bayesian optimization to create more efficient and robust RL reward functions.

Principles

• Quantify code uncertainty to prioritize reward component refinement.
• Decouple reward terms for independent, efficient optimization.
• Bayesian Optimization outperforms direct LLM hyperparameter tuning.

Method

CoUR generates an initial multi-term reward function with an LLM, then uses CUQ to identify and refine ambiguous terms, and finally applies BDO to independently optimize decoupled reward term hyperparameters via Bayesian optimization.

In practice

• Apply CUQ to identify ambiguous reward components.
• Decompose complex reward functions into independent terms.
• Use Bayesian optimization for efficient hyperparameter tuning.

Topics

• Reinforcement Learning
• Reward Function Design
• Large Language Models
• Code Uncertainty Quantification
• Bayesian Optimization

Code references

• Denys88/rl_games

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.