无论文秋招——群核科技一面

业务方向有点像贝壳的室内设计

空间理解

问项目，介绍的比较细

八股

PPO 和 GRPO 的区别

ds zero 1 2 3

做题

计算X,Y的欧氏距离，用torch，需要注意 unsqeeze()扩展维度以便broadcast，以及dim=2

torch.sqrt(torch.sum((X.unsqeeze(1) - Y.unsqeeze(0))**2),dim=2)

import torch

X = torch.tensor([[1.0, 2.0], [3.0, 4.0]])  # 形状 [2, 2]
Y = torch.tensor([[1.0, 1.0], [2.0, 2.0], [4.0, 4.0]])  # 形状 [3, 2]

dist = torch.sqrt(torch.sum((X.unsqueeze(1) - Y.unsqueeze(0)) ** 2, dim=2))
print(dist)

torch.cdist(X, Y)

用 torch.optim.SGD 求 X**2-2x+1的最小值

import torch
def loss_function():
    x = torch.trnsor([5],require_grad=True)
	optimzer=torch.optim.SGD(x,lr=1e-2)
	for i in range(1000):
	  loss= X**2-2x+1
	  optimzer.zero_grad()
	  loss.backward()
	  optimzer.step()
	return x

gpt写法

import torch

def loss_function():
    x = torch.tensor([5.0], requires_grad=True)
    optimizer = torch.optim.SGD([x], lr=1e-2)
    
    for i in range(1000):
        loss = (x ** 2 - 2 * x + 1).sum()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    return x.item()

result = loss_function()
print(result)