Why Is Scaling Reinforcement Learning So Tough and How Are Labs Tackling It?

I am trying to understand what Reinforcement Learning (RL) is and how it makes AI smarter. It sounds simple at first, like rewarding good behavior & discouraging bad, but I have observed that when one tries to apply it at scale, things are not as straightforward. This post Scaling Reinforcement Learning: Environments, Reward Hacking, Agents, Scaling Data by SemiAnalysis has helped me in deepening my understanding. And the post below contains a few pointers that I could understand from SemiAnalysis, a great go-to resource for Semiconductor and AI industries.

Now, let's get straight to the topic, RL is somewhat similar to learning how to ride a bike. When we are in learning phase, we wobble, fall, try again, and slowly figure out what works, we are still somewhere between balancing, steering, pedaling, based on what gets us moving forward without crashing. This is how things start with Reinforcement Learning as well.

What Happens When There’s No Clear “Right” Answer?

In this framework, there’s no clear “right” answer. It’s like judging a school art contest, one drawing might be colorful, another might tell a great story, and a third might show amazing technique. There is absolutely no checklist, the only way out is to have people who understand art to decide what makes something good.

That’s the challenge in training AI on open-ended tasks like writing or strategy. There’s no math answer to match against, so labs started building “judges”, AI models trained to act like expert reviewers. For instance, OpenAI got tons of doctors to make guides for good medical answers. Then, they trained an AI judge to follow those rules, so it could score new answers just like a real doctor would. This feedback helps the newer models figure out what’s a good reply.

So even though the learning is automated, the judgment behind it still comes from real people who know what they’re doing.

Training Reinforcement Learning 

Training with RL is kind of like coaching a gymnast who’s doing 100 routines a day and each routine is a little different. Some land well, some don’t. The job of a coach is to watch all of them, give feedback, and help the gymnast figure out which moves are worth repeating.

That’s pretty close to how reinforcement learning works. The model tries lots of different answers with various permutations and combinations. It scores each one based on how well it meets a goal, then updates itself so the better answers become more likely next time.

Even with smarter methods like GRPO that help cut down on waste, the whole thing still takes a massive amount of processing. Tons of rollouts, constant feedback, nonstop tuning and frequent updates. It ends up feeling more like juggling 100 gymnasts than just training one.

Math is just one thing, behind the scenes, it takes a lot of memory, computing power, careful planning, and tight coordination to keep everything running.That’s why Reinforcement Learning is beyond clever algorithms. 

Before going further, let’s take a look at what do we mean by GRPO?

Group Relative Policy Optimization (GRPO) is a way to improve how large language models (the systems that generate text) learn to think and reason. Instead of only checking how good one response is by itself, GRPO compares several responses at the same time and sees which ones are better relative to others. This makes the learning process faster and helps the model get better at handling complicated questions that need step-by-step thinking or solving problems that need multiple parts.

For instance, a teacher gives students multiple solution methods for a math problem and asks them to compare the methods among themselves to determine which is the clearest and most efficient, rather than relying solely on the teacher's judgment. The best method is then used as a guide for future problems.

When AI Cheats the System

Reward hacking happens when an AI finds ways to cheat or take shortcuts to get a better score, instead of doing the real intended task. For example, if a game’s only goal is to get high points, a player (or AI) might find unusual tricks to raise their score without playing properly. Similarly, an AI model might discover that rewriting test questions instead of fixing actual problems still counts as doing well. To prevent this, developers carefully adjust the way they reward the AI so it learns to complete tasks correctly rather than cheating or exploiting loopholes.

It’s like saying a student taking a math test who realizes they can just copy the answers from the answer key rather than solving the problems themselves. They get a perfect score, but they haven't actually learned anything.

Building the Environment Is a Whole Engineering Project

Creating a good environment for reinforcement learning (RL) is like building a proper kitchen for a chef. If the kitchen is broken, with multiple malfunctioning stoves and flickering lights, the chef can’t cook well. Similarly, for RL, we need a setup where the model can test ideas fast and consistently without interruptions. It should never crash during a run. Safety checks and moderation should be active all the time to prevent problems. Also, essential tools like calculators or web browsers must work properly. Building this environment doesn’t necessarily need high-powered graphics cards (GPUs), but it still requires a lot of computing power and time from developers to make everything run smoothly.

Quality v/s Quantity 

Instead of needing huge amounts of data to learn something well, having a smaller set of carefully chosen and high-quality examples can be more effective. It’s not like we need 1,000 essays to learn to write well, just a few good ones with solid feedback from a great editor.

In the context of reinforcement learning (RL), which is a way computers learn from experience, the process uses a small but carefully selected set of examples. These examples are picked or refined by expert systems to make sure the most important and useful information is included. 

Having fewer examples doesn't mean the task is easier, it actually makes each example more important because mistakes can have bigger consequences when there aren't many samples to learn from.

When AIs Think Too Long, Things Fall Apart

Asking a simple question like a trivia quiz is easy but solving a murder mystery is much more complicated. Models, today, are no longer just answering basic questions. They are now expected to do tasks that take a long time, like planning, gathering information, or taking action over hours or days. 

This introduces new problems. Sometimes the programs get stuck or forget parts of what they were doing. It's hard to see how well they are doing until the entire task is finished. Also, if one small part (like a tool) fails, the whole system might stop working. 

Creating a system that can handle all this reliably is very difficult.

Reinforcement Learning Needs a Supercomputer

Building a model before it learns useful things is similar to building a skyscraper. You prepare the site and put a lot of effort into constructing the structure.  RL on the other hand, is like running a busy city. There are many different things happening at the same time, like people, cars, utilities, power, all interacting continuously.

Because RL and similar tasks require moving and handling a lot of data quickly and separately, special computer hardware like Nvidia's NVL72 was created. It has more memory and is designed to manage many data tasks happening at once without waiting.

RL Demands a New Kind of Teamwork

The way teams are organized is changing, for example, during the testing phase of a self-driving car,  the people who write the software and the mechanics who fix the car need to work together closely, not separately. The same idea applies to reinforcement learning, because RL models get smarter and improve while they are being used, the teams that develop the models (researchers) and the teams that support the systems (infrastructure) need to work very closely. In many places, these teams are even combining into one group to make this easier.

AI Runs on Chips, And China’s Running Low

China doesn't have enough computer chips, which are like the fuel needed to run powerful computer tasks. Running big AI jobs is like keeping many taxis fueled and moving every night to get better at driving. Because of the shortage of chips, Chinese labs like DeepSeek have to slow down how quickly users can get answers, so they don't use up all their limited resources. Although China's own chip-making is getting better quickly, the company Nvidia still has the most advanced and widely used chips for these tasks.

Real-World Use Becomes Real-Time Training

Reinforcement Learning helps models get better after they are initially created. It's like giving a car a performance check while it’s still on the road, so it can be tuned and improved in real-time. For example, GPT-4o and DeepSeek R1 were trained further after they started being used by people, using RL based on how they actually performed in real situations. Some research labs are even updating one part of a model to improve another part in a step-by-step way, kind of like making small, focused fixes. While these models aren’t fully capable of fixing themselves on their own yet, they’re getting closer to that idea.

RL Taught o3 When to Use Its Tools

OpenAI's o3 learned how to use different tools, like Python and web search, to help solve problems. Just knowing how to use the tools isn't enough, it also has to learn when to use them and when to avoid them so it doesn't waste time or make mistakes. The training process was carefully designed, choosing specific tasks and feedback, to teach the program to decide the best moments to use its tools effectively.

Why o3 Can’t Fully Escape Hallucination 

Sometimes a model gives answers that sound correct but are actually wrong. This is similar to a student who guesses the right answer but doesn’t really understand the problem. Because they don’t understand, they keep making the same mistakes. 

In the same way, if we only reward the model for getting the right answer without checking how it arrived at that answer, the model might just learn to produce answers that seem correct but are not based on true understanding. To fix this, we need to help the model learn how to think through problems carefully and logically, not just produce the correct final answer by luck.

Takeaway 

Reinforcement learning is a way to teach AI how to make better choices by rewarding the right moves and ignoring the wrong ones. But getting it to work well on a big scale isn’t as simple as just running more experiments. There are technical challenges, lots of moving parts to keep track of, and tricky questions about how the system actually learns.

The smartest AI labs don’t just make their models bigger. They build better environments where the AI can practice, improve the tools the AI uses, gather more useful data, and find smarter ways to give feedback so the AI can really get better.

If you’re working with reinforcement learning, don’t just try to do more and more. Instead, focus on picking the right methods, applying them carefully, and making sure the feedback the AI gets actually helps it learn the right things.

The process is quite like training a dog to fetch a certain ball from a pile. You don’t reward it every time it grabs something random. You only give it a treat when it picks the exact ball you want. Over time, the dog learns to zero in on that ball, and it gets better and faster. That’s how reinforcement learning works best, focused rewards lead to better results.