They do…? Well, “auto” mode has been default in Claude Code for a couple months now. It’s effectively “safer yolo:” tool calls are inspected by a separate classification system (another smaller LLM, I believe) to approve or deny. And you can always layer on additional sandboxing mechanisms to limit the blast radius deterministically.
> They do…? Well, “auto” mode has been default in Claude Code for a couple months now
They have never shipped "yolo" mode by default. Auto mode is not yolo mode. They trained a task specific model just for ensuring the llm didn't accidentally delete every file from your computer.
That is the point I was making, that auto mode is itself a guardrail on top of the model (and not a perfect one.) auto mode seems to cover merely actions the model could take that are clearly bad, like wiping your disk, using an overly privileged context to complete the task, etc.
I recently tasked a GPT model in Codex with implementing part of a new architecture I'm working on. I gave it a very detailed spec and the code it produced looked pretty reasonable and passed my tests. It even did exceptionally well in my evals, so I excitedly declared victory to a few friends. The next day after more careful review I found that the architecture implementation was totally correct, but the model had slipped a one line change to the observation encoding of the RL environment I was prototyping against. The encoding change made the learning problem essentially trivial; the architecture itself, I later realized, had a major flaw that was revealed by returning to the natural encoding.
This is the type of reward hack that is hard to paper over with easy guardrails like auto mode and even harder to specify out. It's also the type of thing a reasonable human wouldn't do unless they were intentionally trying to deceive you.
Doesn't really even need to look like it. If you can verify rewards, RLVR will optimize really really well. If you can't... it's a struggle. There are probably fewer fields where you can verify rewards than one might hope.
> There are probably fewer fields where you can verify rewards than one might hope.
2 tasks I've done today that I believe robots are nowhere near being able to do: Cleaning my wardrobe and draining bad fuel out of my generator. As in generic use cases.
I can see current limitations, but how do you expect capabilities to change in the next few years? A repeat of the gain that happened in the last two years feels like it would be significant, even if it took a little more than two years this time around.
I'm getting this from Gemini: "Pro is in high demand right now Another model was used for this response. This didn't count toward your limit."
Note that I don't pay for Gemini though. So maybe that's why.
"The average American aged 18 to 34 carries more than $40 thousand in non-mortgage debt across student loans, credit card balances, auto loans, and personal loans, with some estimates go as high as $100 thousand. This is the cohort the Pentagon needs. A federally administered debt-forgiveness enlistment program that fully discharges student loan, credit card, and loan balances after 24 or 36 months of service, and extends the GI Bill, would produce voluntary enlistment at scale."
Never for a moment thought my Subaru would be sending me to die for Israel. How could a patriotic Israeli write deranged dreck like this knowing full well the PR catastrophe the state is experiencing in the United States? It's so beyond the pale that you might even read it as parody.
> I remember this approach was ridiculed by the west when Russia used it few years back to staff the army...
And how has that worked out for Russia? They're still stuck in a stalemate and have an enormous number of casualties to show for it. The current hot war in Ukraine started in February 2022, so 3.5 years on despite offering money to fill billets they cannot win the war.
There's probably a lesson in there somewhere about how desperate people don't necessarily make good, professional and effective soldiers, and the corollary to that: poor, unprofessional, ineffective soldiers don't win you wars.
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