71–80 of 104 posts
Bad title. I thought RL = Real Life, instead of Reinforcement Learning. It should be clearly indicated in the title.
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> Whoever wrote this doesn't seem to fundamentally grasp what they are saying. RL != only online learning. There's a ton of research on offline and imitation-based RL where the training data isn't tied to an agents past policy - which is exactly what this article is pointing to.
I'm not sufficiently familiar with the details on ML to assess the proposition made in the article. From my understanding, RL is a tuning approach on LLMs, so the outcome is still the same kind of beast, albeit with a different parameter set. So empirically, I actually thought that the lead companies would already be strongly focused on improving coding capabilities, since this is where LLMs are very effective, and w…
What you're referring to is actually just one application of RL (RLHF). RL itself is much more than that
The "GPT-3 moment" framing is a bit hype-y I think? GPT-3 eliminated the need for task-specific fine-tuning, but from the article RL wouldn't replace LLM-style pretraining. So this is more of an incremental advance than the paradigm shift GPT-3 represented. That said, if it unlocks RL generalization that would be huge. The core claim that massive-scale RL will unlock generalization doesn't seem that surprising since…
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This article stands as complete hype. They just seem to offer an idea of "replication training" which is just some vague agentic distributed RL. Multi-agent distributed reinforcement learning algorithms have been in the actual literature for a while. I suggest studying what DeepMind is doing for current state of the art in agentic distributed RL.
I didn’t think it was vague. Given an existing piece of software, write a detailed spec on what it does and then reward the model for matching its performance. The vague part is whether this will generalize to other non software domains.
A much harder task than writing said software
I’ve been exploring this too, since I rely on LLMs a lot to build software. I’ve noticed that our dev loop-writing, testing-is often mostly human-guided, but language models frequently outperform us in reasoning. If we plug in more automation; MCP tools controlling browsers, documentation readers, requirement analysers, we can make the cycle much more automated, with less human involvement. This article suggests scal…
> The main barrier is cost I very much disagree. For the larger, more sophisticated stuff that runs our world, it is not cost that prohibits wide and deep automation. It's deeply sophisticated and constrained requirements, highly complex existing behaviors that may or may not be able to change, systems of people who don't always hold the information needed, usually wildly out of date internal docs that describe the s…
Imagine if...nevermind.
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There are 2 kinds of people. Those who are outperformed on their most common tasks by LLMs and those who aren’t.
there are also two kinds of people - those who are excited by that and those who are not. The result is a 2x2 matrix where several quadrants are deeply concerning to me.
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Yea I don't understand how people are "leaving it running overnight" to successfully implement features. There just seems to be a large disconnect between people who are all in on AI development and those who aren't. I have a suspicion that the former are using Python/JS and the features they are implementing are simple CRUD APIs while the latter are using more than simple systems/languages. I think the problem is th…
I'd really like to know which use cases work and which don't. And when folks say they use agentic AI to churn through tokens to automate virtually the entire SDLC, are they just cherry picking the situations that turned out well, or do they really have prompting and workflow approaches that indeed increase their productivity 10-fold? Or, as you mention, is it possibly a niche area which works well? My personal experi…
I haven't seen an LLM stay on task anywhere near that long, like...ever. The only thing that works better left running overnight that has anything to do with ML, in my experience, is training.
I’ve been exploring this too, since I rely on LLMs a lot to build software. I’ve noticed that our dev loop-writing, testing-is often mostly human-guided, but language models frequently outperform us in reasoning. If we plug in more automation; MCP tools controlling browsers, documentation readers, requirement analysers, we can make the cycle much more automated, with less human involvement. This article suggests scal…
> The main barrier is cost I very much disagree. For the larger, more sophisticated stuff that runs our world, it is not cost that prohibits wide and deep automation. It's deeply sophisticated and constrained requirements, highly complex existing behaviors that may or may not be able to change, systems of people who don't always hold the information needed, usually wildly out of date internal docs that describe the s…
So...like SAP then?