I have a guess.
It should need two queues. One for receiving Qr and one for sending Qs.
Assuming Qr and Qs are inverted for worker run and the main, we can write:
def run(Qr, Qs):
    while True:
        b = Qr.get()
        torch.randn(..., out=b) # reusing the buffer tensor without re-allocating
        Qs.put(b)

def main():
    ...
    for i in range(buffer_size): # allocate buffers
        b = torch.empty(...)
        Qs.put(b)
    ... start processes ...
    while True:
        b = Qr.get() # main's Qr = run's Qs
        ... use b ...
        Qs.put(b) # main's Qs = run's Qr

In this fashion, there should be no more than buffer_size tensors in the system. They are always reused and circulated.
Caveat: I see the cost of an additional queue to be huge. It might not be worth the benefit of not reallocating additional shared memory.