Because reinforcement learning supplies a correctness signal without requiring the model to match all reasoning tokens, its training signal has a much higher signal-to-noise ratio.

“ignores all the other bits so in sft you like have like you know to try and match like the exact reasoning tokens that the model produces so you're essentially getting like too many bits about like the exact way this other model you're training on reasons for rl you only get the one bit and that means that like this that signal is not drowned out in the noise of like all the other bits the model has and so that's what really like it's really super dramatic like increase into the signal to noise ratio during training which is why like rl is like”

“It's like so dramatically efficient in terms of steps.”