Posted by lawrenceyan 4 days ago
This will work in a sense. It will do… something… and learn… something. It will be unrelated to the physical universe in any way. See also: procedural landscape generators, etc.
One network typically generates tasks for the other, and is rewarded if it manages to make the other network fail the task. The other network is rewarded if it successfully completes the task.
Thus the adversarial network tries to find weaknesses to exploit, and the combined training makes the solving network much stronger. Or at least that's the idea.
[1]: https://en.wikipedia.org/wiki/Generative_adversarial_network
Ahh, GPT-4o is the arbiter.
So, basically, this is a way to perform LLM model compression (GPT-4o to qwen3) while maximizing the in-distribution domain size. As such, it seems reasonable and useful.
However the reliance on an arbiter LLM makes the claim that it will overcome the problem of a lack of training data unreasonable. Once the target LLM is scaled up to reach the in-distribution domain size of the arbiter, it seems to me it will turn back into a hallucination amplifier.
>> To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch.
Giving the benefit of the doubt, they're just using it wrong, but the way they use it sure reads like they claim they found a way to initialise LLMs with 0 data. Only the absurdity of the claim protects the reader from such misunderstanding, and that's never a good thing in a research paper.
However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-tuning or reinforcement learning, which poses a fundamental bottleneck to advancing AI systems toward capabilities beyond human intelligence
To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch.
Starting from a single base LLM, R-Zero initializes two independent models with distinct roles, a Challenger and a Solver.
Training a LLM is a multi-stage process[1], and they're tackling the stage at the end. That's where you do fine-tuning or reinforcement learning. They're not training a LLM from scratch. They're explicitly stating they start from a base LLM, ie a pretrained non-tuned model.
As I understand it, and as they mention, training data for the latter stages has typically required high-quality human-curated samples in large numbers, even if they're augmented using LLMs, say by generating multiple variations of each human-curated training sample.
Their proposal is to have a generative adversarial network generate that data without any initial human input, ie from scratch.
[1]: https://snorkel.ai/blog/large-language-model-training-three-...
[1]: https://en.wikipedia.org/wiki/Colossus:_The_Forbin_Project