How RLEF gives your AI coding assistant an upgrade

You know how feedback helps people learn? Like teaching a child to ask nicely instead of yelling — it’s all about clear, helpful corrections. That’s how modern AI tools, like ChatGPT, learn by using Reinforcement Learning with Human Feedback (RLHF).

While RLHF is great for conversations or simple tasks, it struggles when tasks get technical, like coding or solving tough math problems. Why? Coding errors need precise, technical feedback — and RLHF just isn’t designed to handle that level of detail.

That's where Meta's Reinforcement Learning with Execution Feedback (RLEF) comes in.

Instead of relying on vague human ratings like "good" or "bad," RLEF uses test-driven feedback based on how the code actually runs. AI, then, is no longer guessing— it's debugging itself.

RLEF doesn't just teach models to code — it makes them code smarter. And that means better AI assistants, more productive developer teams, and maybe even more time for side projects or creative ideas.

Today, I'm breaking down:

• Why RLFH works (until it doesn't)

• Where (and how) RLEF comes in

• How RLEF works

• Why RLEF will make us better developers

Let's go.

Why RLHF works... until it doesn’t

Let's break it down with a simple example. Imagine we're training an AI chatbot named The Joker to tell jokes. The Joker starts off clueless about what’s funny. We give it a classic prompt:

You: “Why did Darth Vader cross the road?”
The Joker: “To get to the dark side.”

Not bad! You chuckle and say, “Alright, that’s a solid 8 out of 10.” The Joker takes note: Star Wars references and clever twists seem to work. So, it tries another one:

You: “Why did Darth Vader cross the road?”
The Joker: “Because the Emperor told him to.”

Eh, not as funny. Maybe a 4 out of 10. The Joker learns that “blind obedience” jokes don’t quite land as well as wordplay. With enough feedback, The Joker starts nailing its punchlines and becomes a decent comedian.

That’s RLHF in action: the AI generates a response, humans provide ratings, and over time, the model improves based on what people like or don’t like. But here’s the problem: this works great for something like jokes, where the feedback is subjective and simple. You know what’s funny, and you can rate it easily.

But now, let’s say The Joker tries solving a math problem or writing Python code. If it outputs something wrong, like a piece of code that doesn’t run, you might say, “That’s incorrect.” But that’s not much help! You probably won’t sit there explaining, “Actually, you forgot a semicolon, and also, your algorithm doesn’t handle edge cases for negative numbers.”

RLHF falls off when the task requires detailed, technical feedback. It’s like trying to teach someone chess by just saying, “Bad move” without explaining why. The Joker is left floundering, clueless about how to improve.

Teaching GenAI to fix its own bugs

Now, imagine The Joker takes a new approach to coding. Enter Reinforcement Learning with Execution Feedback (RLEF). Instead of relying solely on vague human ratings, it uses execution feedback — specific, actionable advice based on how the code performs.

Let’s say The Joker writes a function to calculate prime numbers. It runs the code and gets this feedback:

• Syntax check: “You forgot a colon on line 5.”

• Logic problem: “Your code says 9 is a prime number. It’s not.”

• Performance issue: “Your solution takes too long for inputs larger than 10,000.”

Armed with this information, The Joker doesn’t just throw spaghetti at the wall and hope it sticks. It refines its code, tests it again, and learns from every mistake. Over time, The Joker doesn’t just guess better — it codes smarter. That’s the magic of RLEF.

How RLEF works

Let’s break it down step by step. Imagine you’re teaching an AI model to solve tricky coding problems—like the ones humans tackle in programming competitions. These aren’t your basic “Hello World” tasks. These are brain-teasers that demand creativity, logic, and efficiency. That’s where RLEF shines.

Meta tested RLEF using CodeContests, a benchmark designed to mimic competitive programming. It includes public test cases, shared with the model during training to provide execution feedback for iterative improvement, and private test cases, hidden during training and used solely for final evaluation. 

Unlike public cases, private cases don’t provide feedback—they ensure the model performs well on entirely unseen challenges, testing its true generalization. Problems like "Write a function to find the largest palindrome in a string" demand not just correctness but speed and handling of edge cases under tight constraints. 

👉 Fun fact: RLEF’s learning depends heavily on the quality of its public and private test cases. If these cases are too simple or incomplete, the model could pick up bad coding habits.

 

That’s why domain experts carefully review and test every case to make sure they cover real-world challenges and push the model to learn the right patterns. Quality in, quality out!

Let’s see how RLEF tackles this, step-by-step:

1. The problem statement
   The model gets the task in plain English, like: “Write a function to find the largest palindrome in a string.” The AI doesn’t just have to translate that into code — it needs to understand what the problem is asking for.

2. The initial solution
   The model takes a crack at it. Maybe it writes some code that seems promising. But as any coder will tell you, the first draft is almost never perfect.

3. Execution feedback
   Here’s the game-changer. The code runs against public test cases — examples designed to catch common mistakes. If it fails, the model doesn’t get a vague “Try harder” note. Instead, it receives specific feedback, like:

   1. “Fails on input ‘abba.’ Output should be 4, but your code returned 3.”

   2. “Your solution exceeded the time limit on this specific input.” This feedback is pure gold for learning. The AI knows exactly what went wrong and can use that information to improve.

4. Iterative refinement
   Armed with feedback, the model tweaks its code and runs it again. It’s a cycle of trial and error — write, test, fix, repeat — until it passes the public tests.

5. The final boss: Private tests
   But passing public tests isn’t enough. The code then faces private test cases—hidden challenges designed to be even tougher. If it fails here, the model receives no further feedback and the solution is deemed incorrect, prompting the need for improvement in subsequent training cycles. If it conquers these, you know it’s ready for the real world. This step calculates a pass/fail outcome for all test cases, converts it into a reward signal, and passes it to PPO for policy optimization which in turn optimizes the LLM.

💡 Good to know: Generated code runs in a secure sandbox using Python 3.10. We enforce strict limits — like 1GB of memory and 10 seconds max per test case — to keep things safe and accurate.

 

The sandbox doesn’t just test the model’s code — it also collects detailed feedback from errors, stack traces, and test results. This data helps the model learn and improve through every iteration, all in a controlled, repeatable environment.

Now, here’s where RLEF takes things to the next level. You’ve got the model improving through feedback, but how do you ensure it learns the right lessons and doesn’t pick up bad habits along the way? This is where Proximal Policy Optimization (PPO) comes in.

Think of PPO as the AI’s personal coach — someone who’s always whispering in its ear:

• “Focus on what works.”

• “Don’t waste energy on random guesses.”

• “Avoid bad habits, like writing overly complex or invalid code.”

PPO helps fine-tune an AI’s strategy by guiding its decision-making process. It balances trying new approaches (exploration) with sticking to what already works (exploitation), like teaching a chess player when to take risks or play it safe. Instead of overcorrecting after every mistake, PPO makes gradual adjustments, ensuring steady improvement without losing past progress. 

Combined with RLEF’s detailed feedback from test cases, PPO creates a powerful learning loop where the model efficiently refines its skills and avoids common pitfalls—transforming from a clumsy coder into a highly capable problem solver.

Why do these experiments matter?

Let’s dive deep into the performance of RLEF-trained models published by Meta on the challenging CodeContests benchmark and how they stack up against prior work, including AlphaCodium and MapCoder. The results in the following table showcase RLEF’s transformative impact, significantly boosting the performance of Llama models across varying computational budgets and sample sizes.

But how do we measure success in this arena? It might sound complicated, but it’s actually pretty straightforward:

• n@k measures the probability that at least one of n solutions is correct out of k attempts.

• For example:

  • 1@3 means the model gets up to 3 tries but only needs to get 1 correct.

  • 10@100 means the model generates 100 solutions, and 10 are checked for correctness.

This way, we can fairly compare models even if they’re given different numbers of attempts to solve a problem. Think of it like judging a gymnast not just on their best routine, but also on how many tries it took to nail it. Got it? Great. Let’s dive into the results.

Take Llama 3.0 8B as an example. On its own, it barely scrapes by, with a test set solve rate of 3.2% (1@3). Add RLEF to the mix, and suddenly it’s solving 12.1% (1@3)—nearly a quadruple improvement! That’s not just incremental; it’s transformative.

Similarly, Llama 3.1 8B, a newer version of the 8B model, jumps from 10.5% to 16.0% (1@3) with RLEF. These improvements show that RLEF isn’t just for the big guns — it’s a game-changer for compact models too, making them much more capable without breaking the computational bank.

Now let’s talk about the heavyweight: Llama 3.1 70B. Out of the box, this model already performs well, with a test set solve rate of 27.5% (1@3). But after training with RLEF, it rockets to 40.1% (1@3). That’s nearly a 50% boost!

What’s even more impressive is its performance in larger sample budgets. At 10@100, the RLEF-enhanced 70B model reaches a staggering 54.5% solve rate, making it one of the best-performing models in the entire CodeContests benchmark.

Here’s where RLEF truly shines: compared to prior approaches like AlphaCodium and MapCoder, RLEF-trained models achieve state-of-the-art performance while being far more efficient. AlphaCodium, built on GPT-4-based frameworks, combines advanced techniques like chain-of-thought prompting, program repair, and automatic test generation. But here’s the problem: it relies on large sample sizes (100 or more) to achieve decent performance. That’s computationally expensive and inefficient.

RLEF flips the script by using execution feedback to deliver better results with fewer samples.

It’s not just about solving more problems — it’s about solving them smarter, faster, and more efficiently. This isn’t brute force; it’s precision engineering.

We can see that RLEF isn’t just a small tweak — it’s most likely a paradigm shift. By adding execution feedback, it helps models learn from their mistakes in a way that’s both precise and iterative. The numbers in the table don’t just show improvement—they show transformation. Whether it’s a modest 8B model or a massive 70B powerhouse, RLEF brings out the best in every Llama and potentially in other models that adopt this innovative approach. 

Turning feedback into future-ready code

When it comes to comparing RLEF to methods like AlphaCodium and MapCoder, the difference is clear: RLEF-trained models aren’t just good —they’re leading the pack while staying efficient. With RLEF, the future of AI isn’t just about generating answers — it’s about learning to solve problems smarter, faster, and better. And that’s a future worth coding for. 

As developers, we stand at a crossroads. GenAI and advancements like RLEF are reshaping how we code, debug, and build software.

I’ve been where you are — wondering how these changes will affect my work, my skills, and my future. Here’s the truth: understanding GenAI tools like RLEF isn’t just an advantage — it’s becoming a necessity. At Educative, we’re here to help you not only adapt but thrive in this new landscape. Explore our courses, dive into GenAI, and learn how to code smarter, faster, and better. 

The future of software development is here, and it’s waiting for you.