RL is more information inefficient than you thought
17 comments
·November 27, 2025andyjohnson0
quote
I, too, started parsing this as RL=real life and that’s why I found the headline interesting
Angostura
Thank god. Was driving me mad.
on_the_train
It's a deliberate click/ragebait, not a mistake. It makes People click and talk about it, just like it happens here.
gpvos
Never attribute to malice that which is adequately explained by stupidity.
bogtog
The premise of this post and the one cited near the start (https://www.tobyord.com/writing/inefficiency-of-reinforcemen...) is that RL involves just 1 bit of learning for a rollout, rewarding success/failure.
However, the way I'm seeing this is that a RL rollout may involve, say, 100 small decisions out of a pool of 1,000 possible decisions. Each training step, will slightly upregulate/downregulate a given training step in the step's condition. There will be uncertainty about which decision was helpful/harmful -- we only have 1 bit of information after all -- but this setup where many steps are slowly learned across many examples seems like it would lend itself well to generalization (e.g., instead of 1 bit in one context, you get a hundred 0.01 bit insights across 100 contexts). There may be some benefits not captured by comparing the number of bits relative to pretraining.
As the blog says, "Fewer bits, sure, but very valuable bits", this also seems like a different factor that would also be true. Learning these small decisions may be vastly more valuable for producing accurate outputs than learning through pretraining.
macleginn
It is the same type of learning, fundamentally: increasing/decreasing token probabilities based on the left context. RL simply provides more training data from online sampling.
macleginn
In the limit, the "happy" case (positive reward), policy gradients boil down to performing more or less the same update as the usual supervised strategy for each generated token (or some subset of those if we use sampling). In the unhappy case, they penalise the model for selecting particular tokens in particular circumstances -- this is not something you can normally do with supervised learning, but it is unclear to what extent this is helpful (if a bad and a good answer share a prefix, it will be upvoted in one case and penalised in another case, not in the same exact way but still). So during on-policy learning we desperately need the model to stumble on correct answers often enough, and this can only happen if the model knows how to solve the problem to begin with, otherwise the search space is too big. In other words, while in supervised learning we moved away from providing models with inductive biases and trusting them to figure out everything by themselves, in RL this does not really seem possible.
sgsjchs
The trick is to provide dense rewards, i.e. not only once full goal is reached, but a little bit for every random flailing of the agent in the approximately correct direction.
thegeomaster
Article talks about all of this and references DeepSeek R1 paper[0], section 4.2 (first bullet point on PRM) on why this is much trickier to do than it appears.
Jaxan
How do you know the correct direction? Isn’t the point of learning that the right path is unknown to start with?
scaredginger
Bit of a nitpick, but I think his terminology is wrong. Like RL, pretraining is also a form of *un*supervised learning
cubefox
Usual terminology for the three main learning paradigms:
- Supervised learning (e.g. matching labels to pictures)
- unsupervised learning / self-supervised learning (pretraining)
- reinforcement learning
Now the confusing thing is that Dwarkesh Patel instead calls pretraining "supervised learning" and you call reinforcement learning a form of unsupervised learning.
pavvell
SL and SSL are very similar "algorithmically": both use gradient descent on a loss function of predicting labels, human-provided (SL) or auto-generated (SSL). Since LLMs are pretrained on human texts, you might say that the labels (i.e., next token to predict) were in fact human provided. So, I see how pretraining LLMs blurs the line between SL and SSL.
In modern RL, we also train deep nets on some (often non trivial) loss function. And RL is generating its training data. Hence, it blurs the line with SSL. I'd say, however, it's more complex and more computationally expensive. You need many / long rollouts to find a signal to learn from. All of this process is automated. So, from this perspective, it blurs the line with UL too :-) Though it dependence on the reward is what makes the difference.
Overall, going from more structured to less structured, I'd order the learning approaches: SL, SSL (pretraining), RL, UL.
thegeomaster
You could think of supervised learning as learning against a known ground truth, which pretraining certainly is.
Davidzheng
a large number of breakthroughs in AI are based on turning unsupervised learning into supervised learning (alphazero style MCTS as policy improvers are also like this). So the confusion is kind of intrinsic.
Since it is not explicitly stated, "RL" in this article means Reinforcement Learning.
https://en.wikipedia.org/wiki/Reinforcement_learning