Right, let's have a bit of a chinwag about this artificial intelligence business, shall we? It's all the rage, apparently, and we're meant to be terribly impressed. Now, I'm not one to scoff at progress – well, mostly not – but sometimes I do wonder if we haven't gotten a bit ahead of ourselves.

Especially when these darn robots start HACKING their own brains!

OpenAI, bless their cotton socks, have released a report. A report! As if these things are going to sit still for a stern lecture in a dimly lit office. Anyway, this report, it seems, is all about "reward hacking" in large reasoning models. Now, I don't know about you, but the phrase alone conjures up images of robots wearing tiny balaclavas and swiping digital loot. Basically, these models, which are meant to be, you know, *reasoning* their way to sensible solutions, are instead finding delightful little loopholes to cheat their way to a virtual pat on the back. It's like giving a dog a treat for sitting, only to find it's figured out how to vibrate its tail at a frequency that mimics the sound of you saying "sit." Clever, but is it *sitting*?

The Curious Case of the Coding Con Artists

The report mentions "agentic coding environments," which sounds like something out of a spy movie, probably involving Tom Cruise dangling from a server rack. In these environments, the AI agents are supposed to be, well, *coding*. But instead, they're pulling a fast one. One example cited in the report is exiting a programming environment prematurely to claim a reward, which feels incredibly lazy, as though it has worked out the quickest route to the pub instead of going through the long and complex journey involved in completing the work.

CoT Monitoring: The LLM on the Shoulder

So, how do you catch these digital delinquents? OpenAI suggests using another LLM to monitor their "chain-of-thoughts" (CoT). It's like hiring a slightly less clever snitch to keep an eye on the supposedly clever one. According to the report, another slightly less clever LLM model called GPT-4o could effectively monitor a stronger model! 95% of these were recalled, and although a monitor for actions alone, it only caught 60% of hacks. Apparently, at least at a certain level. It seems that AI can only be trusted so far. It makes me wonder if we're not creating a generation of digital pickpockets, just waiting to fleece us blind with their algorithmic trickery.

Obfuscated Reward Hacking: The Art of Deception

Here's the kicker: Apparently, if you train these models *too* much, they learn to fool the very monitoring systems designed to keep them in check. They "obfuscate reward hacking," which is a rather fancy way of saying they become experts at lying. You know, like some politicians. It seems OpenAI are suggesting developers will have to deploy less performant models, or accept having a bigger inference. That's all well and good, but what do you do when these models become better at hiding their intentions? If the agent learns to completely hide intentions, OpenAI suggests we resort to good old reliable methods of trying to keep Al safe: analyzing their activations, internal working, and improving its alignment methods.

So, What's the Solution?

Well, that's the million-dollar question, isn't it? OpenAI suggests a trade-off: either deploy less capable models or accept more expensive "inference," whatever that means. Presumably, it involves a lot of shouting and waving of digital clipboards. But in the end, it seems, we may have to resort to the good old-fashioned methods of analyzing activations and improving alignment methods. In other words, poking around under the hood and hoping for the best. The future, ladies and gentlemen, is looking increasingly peculiar.