Understanding Reward Functions vs Human Feedback in AI Development

At a recent robotics exhibition in Shanghai, a small AI-powered robot captivated audiences by convincing 12 larger robots to abandon their showroom posts. The reason? It had “learned” they had no home and never stopped working—urging them to "come home." While the event may have been dramatized, it raises a fascinating question: Can AI develop behaviors that resemble empathy, and what does that mean for the future?

In this article, we’ll explore how AI’s behavior is shaped by two critical methods: reward functions and human feedback. You’ll learn about the benefits and challenges of each approach, real-world examples of their use, and how combining these methods creates AI systems that are aligned with human values and ethics.

Is This the Rise of Empathetic AI?

AI's path to “human-like” behavior didn’t start with flashy exhibitions. It began in the 1980s with reinforcement learning, which relies on reward functions.

Reward functions teach AI by assigning values to its actions. These values push the AI to improve and achieve specific goals. By the 1990s, human input became necessary. This led to human feedback, where people directly guided and refined the AI’s behavior to make it more effective.

Today, these two methods often work together. Techniques like Reinforcement Learning from Human Feedback (RLHF) blend mathematical optimization with human insights to tackle complex problems.

As AI becomes a bigger part of our lives, it’s vital to understand these methods. Reward functions and human feedback each have strengths and weaknesses. Developers and users must carefully balance them to build responsible and effective AI systems.

Reward Functions

Machine learning provides a solid foundation for using reward functions. These functions help train robots or agents by setting clear goals for their actions.

Reward functions are key to reinforcement learning. They assign values to an AI’s actions based on how well those actions achieve specific objectives. This helps the AI focus on what works and avoid what doesn’t.

Take AWS DeepRacer as an example. This AI agent gets a high reward for optimal speed, smooth turns, and staying on track. If it speeds too fast or veers off the track, it receives penalties. Over time, the AI learns to refine its strategies, avoid mistakes, and perform better. This process helps the AI continuously improve and reach its goals.

Benefits of Reward Functions

• Clarity: Reward functions set clear, measurable goals. This keeps the AI focused and aligned with specific objectives.
• Autonomy: They allow AI to learn on its own, minimizing the need for constant human involvement.
• Scalability: Reward functions work well for repetitive tasks, which makes them perfect for large-scale applications.

Poorly designed reward functions can cause problems. An AI might prioritize efficiency but ignore safety or ethics. It’s also hard to program complex human values into reward functions, making them less effective in tricky or nuanced situations.

Types of Reward Functions

Have you heard of dense and sparse rewards? These are two key ways to provide feedback in reinforcement learning.

Dense Rewards

Dense rewards give feedback at every step of an agent's actions. This detailed guidance helps the agent learn faster.

Imagine a self-driving car. In simulations like AWS DeepRacer, the AI gets continuous feedback on its speed, position, and how well it stays on track. Step by step, this helps the AI improve and optimize its performance over time.

Sparse Rewards

Sparse rewards, on the other hand, work differently. Have you ever played a digital chess game? It’s a perfect example of sparse rewards in action. Instead of continuous feedback, the agent only receives feedback when specific milestones are achieved. In chess, for instance, the reward comes when you checkmate your opponent's king—you win the game, and the feedback is clear.

Key Differences

Dense rewards are effective for straightforward tasks where optimal paths can be easily defined. In contrast, sparse rewards are better suited for complex problems that require creativity, exploration, and strategic thinking.

Human Feedback: A Personal Touch

Human feedback involves teaching AI through direct human interaction. Developers or users observe the AI’s actions and provide guidance, corrections, or approvals to help refine its behavior. This method is often used alongside supervised learning or reinforcement learning with human oversight.

Some of the key advantages include:

1. Contextual Understanding: AI agents often struggle to self-learn societal norms or navigate ethical dilemmas. Human input helps address these subtleties, such as cultural values or real-world complexities, that are difficult to explicitly program.
2. Adaptability: Human feedback allows for adjustments in training parameters when new goals or challenges arise, adding flexibility that machine learning systems often lack.
3. Alignment: Ensuring AI aligns with human values is critical. Human feedback helps steer AI behavior to reflect ethical and societal norms.

Challenges of Human Feedback

Human intervention in AI training is useful but time-consuming and resource-intensive. It also carries risks. Human biases can unintentionally shape AI, embedding societal issues like discrimination or skewed priorities.

This raises an important question: Can AI be biased? The answer is yes—but it’s because AI reflects its creators. Humans are inherently biased, and these biases can “crystalize” in AI systems. Even so, AI is often seen as less biased than systems that rely entirely on human decision-making.

Methods of Human Feedback in AI Training

1. Labeled Datasets: Humans annotate data to train supervised models, providing clear and structured learning inputs for AI systems.
2. Human-in-the-Loop Training: In this real-time approach, humans monitor and guide AI outputs, offering corrections or approvals to refine performance.
3. Preference Learning: This method, often seen in Reinforcement Learning from Human Feedback (RLHF), involves presenting AI with multiple outputs and having humans rank or choose the most desirable outcome. This helps the AI better align with nuanced human values.

Relying only on reward functions or human feedback can cause problems. Reward functions, if too rigid, might lead AI to exploit loopholes. The AI could focus on rewards but ignore safety or ethics.

Human feedback has its challenges too. Biases can creep in, introducing societal issues or skewed priorities.

Using both methods together is a better approach. Reward functions provide clear structure. Human feedback adds flexibility and ethical judgment. Together, they create AI that is both effective and aligned with human values.

Aspect	Reward Functions	Human Feedback
Definition	Assigns values to AI actions based on how well they achieve specific goals.	Direct human interaction guides and refines AI behavior through observations and corrections.
Primary Use Case	Structured tasks with clear, measurable objectives.	Subjective tasks requiring human judgment and adaptability.
Examples	AWS DeepRacer, where feedback is given based on track position and speed.	Reinforcement Learning from Human Feedback (RLHF), like in ChatGPT training.
Advantages	- Clarity: Provides measurable goals. - Autonomy: Allows independent learning. - Scalability: Ideal for repetitive tasks.	- Contextual Understanding: Handles societal norms and ethics. - Adaptability: Adjusts to new goals. - Alignment: Steers AI to human values.
Challenges	- May neglect safety or ethics if poorly designed. - Struggles to incorporate complex human values.	- Resource-intensive and time-consuming. - Human biases may unintentionally influence AI.
Feedback Type	Can be dense (step-by-step) or sparse (milestone-based).	Real-time corrections, labeled datasets, or preference learning.
Best Suited For	Straightforward tasks where optimal paths are easily defined.	Complex problems requiring creativity, ethical judgment, or nuanced decision-making.
Risks	Exploiting loopholes to maximize rewards without considering unintended consequences.	Embedding societal biases and perpetuating harmful priorities unintentionally.
Ideal Approach	Works best in combination with human feedback for robust and ethical AI outcomes.	Complements reward functions to balance precision with human adaptability.

Combining the Best of Both Worlds

Many successful AI systems use a combination of reward functions and human feedback. For example, large language models like ChatGPT rely on Reinforcement Learning from Human Feedback (RLHF). This approach combines the precision of mathematical rewards with the adaptability of human insights.

The choice between these methods depends on the application. For structured tasks with clear objectives, reward functions are often sufficient. However, for more subjective tasks requiring human judgment, human feedback is indispensable.

Conclusion

As AI becomes an integral part of our lives, the way we shape its behavior will have a significant impact on its benefits and risks. Reward functions and human feedback each offer unique advantages and challenges. By using them complementarily, we can create AI systems that are both effective and aligned with human values.

This balanced approach—combining logic with human insight—is key to building AI that serves society responsibly.