Computation and Language - Can LLMs Translate Human Instructions into a Reinforcement Learning Agent’s Internal Emergent Symbolic Representation?

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're cracking open a paper that asks: Can AI really understand what we mean when we give it instructions?

Think about it like this: imagine you're teaching a puppy a new trick. You might say, "Sit!" The puppy eventually learns that "sit" means a specific action. Now, imagine you're trying to teach that same trick to a super-smart robot. Could you just tell it "sit," or would it need a whole instruction manual?

That's kind of what this paper is about. Researchers were curious if Large Language Models, or LLMs – think GPT, Claude, the big brains behind a lot of AI these days – could translate our everyday language into the internal "symbols" that another AI, specifically one learning through trial and error (like that puppy!), uses to understand the world.

Now, this other AI wasn’t learning to sit. It was navigating virtual environments, specifically an "Ant Maze" and an "Ant Fall" world. Imagine a little ant robot trying to find its way through a maze or trying not to fall off a cliff – cute, right? This ant-bot uses something called hierarchical reinforcement learning, which basically means it breaks down complex tasks into smaller, more manageable chunks. Each of these chunks gets assigned a kind of internal "symbol" or representation.

Here's where it gets interesting: the researchers wanted to see if they could feed natural language instructions, like "go to the red block," to the LLMs, and have the LLM figure out which of the ant-bot's internal symbols best matched that instruction.

Think of it like trying to find the right file folder on your computer when you only have a vague idea of what you named it. You have to guess which folder structure best matches your mental picture, right?

So, what did they find? Well, the LLMs could translate some of the instructions into the ant-bot's internal symbolic language. But, and this is a big but, their performance varied a lot. It depended on:

• How detailed the ant-bot's internal representation was. If the "chunks" of tasks were too big or too small, the LLMs struggled. It's like trying to find a specific grain of sand on a beach – too much detail!
• How complex the task was. The harder the maze, the harder it was for the LLMs to figure out what was going on. Makes sense, right?

Essentially, the LLMs weren't always on the same page as the learning agent, suggesting a misalignment between language and the agent's internal representation.

Why does this matter? Well, imagine a future where you can just tell a robot what to do, and it actually understands you, not just in a literal sense, but in a way that connects to its own internal understanding of the world. This research shows we're not quite there yet. It highlights the challenges in getting AI to truly "understand" our intentions and translate them into actions.

For researchers, this points to the need for more work on aligning language models with the internal representations of learning agents. For developers, it suggests being mindful of the level of abstraction when designing AI systems that interact with natural language. And for everyone else, it's a reminder that while AI is getting smarter, it still doesn't always "get" us.

This research raises some interesting questions, doesn't it? Like:

• Could we train LLMs specifically to understand the internal representations of different types of AI agents?
• What are the best ways to create internal representations that are both efficient for the AI and easy for humans (and LLMs) to understand?

And, maybe the biggest question of all: how do we even translate our thoughts and intentions into language in the first place? Perhaps understanding that better could help us build smarter, more intuitive AI.

That's all for today's PaperLedge deep dive. Keep learning, crew!

Credit to Paper authors: Ziqi Ma, Sao Mai Nguyen, Philippe Xu