The policy is how you select your actions -- in this case, the next token. It can be random, but it doesn't have to be. "Deterministically choose the best action" is a valid policy (we would call it the greedy policy), as long as you have some other means of injecting stochasticity so the model explores the space. Uniform random is also a valid policy, as is always selecting the same token (it obviously wouldn't be very performant, and would defeat the purpose here, but it might be fine in, for example, a multi-armed bandit scenario). Most of the time, the policy is a parameterized distribution, and we want to learn the model parameters that maximize some measure of success (the reward component).

Off-policy versus on-policy refers to what data the model is trained on. On-policy training is where the training data is collected by the policy. Off-policy training is where the data was collected by a different sampling process (e.g. we have a standard dataset that we're going to use for supervised training).

It’s quite common these days to treat an LLM as a policy in the sense that it takes as a “state” the previous context, and its task is to choose a continuation, as an “action”. It gets a “reward” from a reward model that was trained on human preferences, or from a verifiable source, such as passing test cases.

This framing has been active for several years, as it’s the framing that enables RLHF and RLVR. RLHF itself is quite old, I think since the original chatGPT.

What is this comment? It’s an RL paper, these are standard RL terms

Gemini didn't really say that exactly, did it? Because it's oversimplified to the point of being wrong.

“Policy” here refers to a probability distribution, i.e. a function that, given some context, assigns probabilities to possible next tokens. It's what a model’s behavior looks like when viewed through an RL lens.

The paper discusses “on-policy” and “off-policy” training, which is central to its idea.

Off-policy training is what happens in standard supervised fine-tuning (SFT): the model is trained on examples that were produced independently of the model. This means that the examples have a different distribution than what the model produces. This can have a negative effect on previously learned capabilities.

On-policy training (in this context) uses data generated by the model itself. It samples the model’s own outputs, scores them against whatever results are being trained for, and updates the model based on those scores. This reinforces certain aspects of the model's own pretrained behavior, so is a "gentler" way to change the model's behavior. The authors claim that this reduces "catastrophic forgetting" and other negative consequences of SFT.