Eureka: Automating Reward Design with Coding LLMs
Understand why reward design in reinforcement learning is difficult, human-dependent, and brittle. Explore how Eureka reframes it as a task for agentic automation.
Problem space: Automating reward design for reinforcement learning, using LLM-based agent systems.
Understanding the problem of reward design
In reinforcement learning (RL), agents learn to perform tasks by interacting with an environment, and receiving feedback in the form of rewards. The fundamental goal for an RL agent is to learn a policy that maximizes the total accumulated reward over time. However, this learning process is critically dependent on how well the “reward function” is defined. A reward function acts as a precise rulebook, signaling to the agent what constitutes desirable, or undesirable behavior in the environment. For instance, a positive reward might be given for successfully balancing a robot or picking up an object, while a penalty is applied for falling or dropping it. The agent then uses this feedback to iteratively adjust its actions and improve its performance.
While the concept seems straightforward, designing effective reward functions for real-world RL tasks is notoriously difficult. It’s far more complex than simply assigning a success point, or a failure penalty. Real-world behaviors are often nuanced and involve many subtle elements that are challenging to codify explicitly. For example, teaching a robotic hand to spin a pen isn’t just about the pen rotating. The reward function must also implicitly or explicitly encourage smooth motion, prevent the pen from being dropped, avoid unsafe angles, and possibly account for efficiency or style. If any of these subtle elements are missed, the agent might learn unintended or “exploitative” behaviors, such as flinging the pen too quickly accumulate a high reward. This intricate and often brittle process is known as “reward engineering,” or “reward design.”
This challenge has long been a significant bottleneck in applying RL to complex real-world problems. It demands a combination of deep technical skill, specific domain knowledge, and a substantial time investment in a trial-and-error process that is often inconsistent. Without a well-designed reward function, the agent is effectively learning without any direction, akin to a student trying to pass an exam without a clear syllabus or understanding of what’s being graded. The reward function is the crucial “syllabus” that provides the learning target. Automating this human-dependent and often unreliable process in an intelligent, and safe way is a powerful advancement in agentic system design.
Introducing Eureka: An agent that designs rewards
Recognizing the bottlenecks of manual reward design, NVIDIA researchers proposed a novel solution: an AI agent whose primary task is to automatically design the reward function itself. This is the core innovation behind Eureka.
Think of Eureka as a “reward architect.”
Operational lifecycle
Eureka operates as a self-contained, iterative process. It is initialized with a task description, and environment code. Its core loop runs for a predefined number of iterations (N), continually refining the reward function. Within each iteration, it samples new reward candidates, evaluates them, and updates its ‘best reward’ if a more performant one is found. The process stops after completing the set number of iterations, or if a satisfactory performance is achieved. While designed for autonomous operation, human intervention can also guide its process, as we’ll see in later lessons.
Eureka’s autonomous workflow involves several key steps:
Environment interpretation: Eureka reads the raw environment source code to understand the task’s mechanics and objectives. Examples include balancing a bipedal robot, or dexterously manipulating an object.
Initial reward generation: Based on this understanding, it crafts an initial reward function in code that aims to capture the core success criteria for the task.
Iterative refinement: Eureka then iteratively improves this reward function. It deploys the generated reward to train an RL policy in a simulator, as it observes the policy’s performance. Then, it uses this feedback to identify shortcomings in the reward design.
Intelligent revision: It reflects on failures and successes, analyzing which parts of the reward logic contributed positively or negatively to the learning outcome. It then revises the reward function accordingly until the desired behavior is learned by the RL agent.
What makes Eureka’s approach particularly powerful and “agentic” is its ability to operate without relying on handcrafted reward templates or extensive human tuning for each new task. Instead, it employs zero-shot code generation to produce initial reward logic, integrates self-evaluation to detect flaws, and uses in-context learning to enhance future reward designs.
In essence, Eureka embodies a
It’s important to note that Eureka, in its current form, is designed to solve one reward design task at a time. It learns and refines the reward function within the iterative process for a single task. It does not explicitly retain or generalize learned reward design heuristics between different, unrelated tasks. For each new environment or task, Eureka effectively restarts its reward discovery process from scratch, leveraging its zero-shot capabilities that we will learn in the next lesson.
Why study Eureka: A design perspective
For us, as agentic system designers, Eureka is more than just a clever algorithmic advancement. It offers profound insights into how to build intelligent, adaptive, and autonomous AI systems. Eureka serves as a compelling case study that addresses some of the most critical challenges in contemporary AI system design:
Automating expertise: How can we automate complex tasks that traditionally demand deep domain expertise, such as the nuanced process of reward engineering?
Creative and iterative autonomy: How can we structure agents to operate creatively, iteratively, and with minimal human supervision, allowing them to explore and refine solutions in an open-ended manner?
End-to-end design loops: How can large language models be integrated not merely to respond to direct prompts but to drive and govern entire, closed-loop design processes?
From an agentic system design standpoint, Eureka exemplifies several important agentic capabilities we’ve discussed:
Goal-driven behavior: Eureka’s operation is explicitly goal-driven; its objective is to improve the performance of an RL policy, an inherently dynamic target that requires continuous adaptation and refinement of its outputs.
ReAct (reasoning + acting) pattern: Eureka’s intelligent revision process, where it analyzes training outcomes (observations) to diagnose reward performance (reasoning), generating a refined reward function (acting), strongly aligns with the ReAct pattern. This provides granular feedback for targeted self-correction.
Tool calling loop pattern: Eureka is a prime example of a tool-augmented agent. It effectively leverages external simulators like Isaac Gym as a powerful tool. The LLM continuously operates in a tool calling loop, deciding to invoke the simulator, and observing the policy’s performance. It then uses that feedback to guide subsequent reward generation.
Self-improvement loop: At its core, Eureka operates within a sophisticated self-improvement loop. It generates designs, and evaluates their real-world consequences (via RL training). It then uses that feedback to adapt and enhance its subsequent designs. This continuous refinement is a direct outcome of applying the ReAct and tool calling loop patterns iteratively.
System autonomy: Once initiated, Eureka executes the entire reward discovery and refinement loop autonomously, without constant human intervention. This coordination of multiple steps within a closed feedback loop is a hallmark of highly autonomous agentic systems.
These capabilities are precisely what we aim for when designing robust, scalable, and intelligent agentic systems.
Design goals of the Eureka learning agent
The following are core design goals that define the Eureka agent and distinguish its approach:
Zero-shot code generation: Eureka is designed to generate complete reward functions from scratch, without the need for pre-existing examples or templates. This is analogous to a software developer, who can start writing tests for a new project simply by understanding its source code and requirements.
Environment as direct context: Instead of relying on extensive prompt engineering or manual parameter tuning, Eureka processes the actual environment source code as its primary context. This deep contextual understanding allows it to make highly informed decisions about reward structure, directly reflecting the environment’s true behavior.
Automated testing and iterative improvement: Eureka does not merely produce a single draft. It rigorously tests its generated reward functions within an RL simulator, and observes the resultant policy’s learning performance. It uses this empirical feedback to iteratively refine the reward design in subsequent rounds.
Intelligent reflection on training outcomes: When an RL policy fails to learn effectively, Eureka doesn’t resort to random changes. It employs a sophisticated “reflection mechanism” to analyze which specific components or aspects of the reward function might have hindered or contributed to the learning process. This targeted diagnosis allows for more precise, and effective revisions in the next iteration.
Autonomous operation: From the initial reading of environment code to generating, testing, evaluating, and ultimately refining reward functions, Eureka executes the entire design loop with a high degree of autonomy and without constant human supervision. It functions as a self-directed entity in the reward design process.
These design goals serve as a blueprint for any system aimed at building or improving other agents. In the following lessons, we’ll break down how Eureka accomplishes each of these, from its use of raw environment context to its multi-round reward reflection mechanism.
Architectural overview of Eureka
To solve the complex challenge of automating reward design, Eureka employs a sophisticated architecture that integrates a large language model (LLM) with a reinforcement learning (RL) simulation environment in a closed-loop system. As shown in the illustration below, Eureka’s core design involves key components such as a coding LLM, environment code, task description, GPU-accelerated RL, and a reward reflection mechanism. These are all interacting to facilitate the autonomous discovery, and refinement of reward functions.
Eureka takes unmodified environment source code, and language task description as context to zero-shot generate executable reward functions from a coding LLM. Then, it iterates between reward sampling, GPU-accelerated reward evaluation, and reward reflection to progressively improve its reward outputs.
This architecture provides the framework for Eureka’s operation, enabling it to autonomously generate, test, and improve reward code. We will delve into each of these core components, and their dynamic interactions in detail in the upcoming lessons.
Quiz
If Eureka generates a reward function that leads to unsafe or unintended agent behavior, such as a robotic arm spinning dangerously to maximize reward, what is the most appropriate strategy to prevent this?
Add more randomness to the agent’s policy to reduce overfitting to one reward strategy.
Insert a hardcoded penalty in the simulator for any fast or jerky movements.
Implement a runtime behavioral monitor that detects and penalizes unsafe actions during training.
Reduce the size of the LLM used so it generates simpler reward functions.