the algorithms technically, sure, however the outcomes definitely depend on data quality and coverage like any other training method, this is well known

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danielmarkbruce 4 hours ago | parent [-]

I don't think you've ever done either of these training steps. You are just handwaving.

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verdverm 4 hours ago | parent [-]

you know what they say about making assumptions, yea?

and then you are going to ignore all the research and results that clearly show otherwise? why?

what might we infer about the importance of data from a learning algorithm like decision trees?

	▲	danielmarkbruce 2 hours ago \| parent [-]
		Read the paper. They train RLCR on existing big math problems. They subtract a brier score penalty from the correctness reward. No new confidence labels are needed. Existing datasets, different reward function.