RL involves weight updates, but the model is frozen after training - no inference time weight update! An RL-trained model does tend to have generic "reward maximizing" long-term goal behavior at inference time, but it's ability to correctly/fruitfully chain together reasoning steps is much dependent on RL training.
> also i think that it is reasoning exactly like us humans do. Indistinguishable
Yes, it is copying human reasoning so it will appear the same, but the difference is when you don't know what to do/try next - when you are trying to solve a problem that you have never solved before and don't know from experience what to try next. This is when having real/generic ability to reason, not just "reason from memory" matters. This is when things like human curiosity are useful : "I wonder what happens if I try this ..."
> also i think that it is reasoning exactly like us humans do. Indistinguishable
Yes, it is copying human reasoning so it will appear the same, but the difference is when you don't know what to do/try next - when you are trying to solve a problem that you have never solved before and don't know from experience what to try next. This is when having real/generic ability to reason, not just "reason from memory" matters. This is when things like human curiosity are useful : "I wonder what happens if I try this ..."