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Comment by verdverm | Hacker News Reader

You are not engaging with actual points, instead attacking a person based on your bad assumptions and projections.

We both know who is

> just acting in bad faith at this point.

The relevant data is the reasoning trace. Doesn't need user data. You can learn from people's detailed reasoning steps how confident they are, even outside your domain.

Take RL 101. This is a common pattern.

We were talking about Jev and probability, now you're changing the problem, a rhetorical trick some people try to employ.

Another that uses dice rolling, coin flips, and an inventory level example to drive home the point that Jev's output are not real probabilities for outcomes.

https://news.ycombinator.com/item?id=49830385

> Take RL 101

I taught it (ML course; a day on RL, at a university), you should really stop making assumptions friend. Data quality and coverage matters in learning algorithms.

Here's one of the books used in that course https://amlbook.com/

Thinking blocks are not a place you can derive real confidence scores in LLMs

My initial comment and every one following is about RLCR and that paper. You don't appear to grasp the basics of that paper, it's reward function or how the optimizer is updating weights.

You are out of your depth and grasping at straws.

> You are out of your depth and grasping at straws.

Do you have any credentials or evidence that others can use to determine if this statement is not more accurately describing the author who wrote it?

Perhaps a PhD in ML, research output like published papers, or teaching/professional experience - all things I have

We could debate the merits of the paper contents, but I suspect you have intentionally moved on to personal attacks. Regardless, nothing you have said (nor can be found in this paper) has been a counter argument that learning algorithms are sensitive to training data, where the measured output difference is used by the optimization algorithm when updating the parameters. Garbage in, garbage out is a saying for a reason. No algorithm fixes non-representative data.

>you have to have training data with accurate probabilities

This was your claim. If you can't read and understand that paper in relation to your claim, you are out of your depth. You haven't made a single claim relevant to that paper - just hand wavy comments about data.

I clarified multiple times that I meant "accurate, representative data" to "derive accurate probabilities"

you are still employing underhanded techniques in an attempt "win an internet debate" (my impression)

try being more accommodating and flexible over repeating the same lame things

it's not hard to say, "ah I see what you were trying to say..." and move towards a more constructive conversation

RLCR / Jev et al. can only give as accurate predictions and probabilities as the underlying data they are trained on represents. Biased data results in biased probabilities, no algorithm fixes this. Can we agree on this point?

They post train on big math and improve calibration across five different non math benchmarks so the new claim "can only give as accurate predictions and probabilities as the underlying data they are trained on represents" is also off base. RLCR generates its own calibration examples from ordinary questions and answer keys. RL usually isn't trying to represent a data set, it's closer to search.

I'm not expecting you to human RL here, but I do hope next time you remember there is another imperfect human behind the screen and are less too online in future interactions

https://www.youtube.com/watch?v=c1Fv1uKTd-w

oh-seven

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