Week 24, 2026

June 8 - June 14, 2026

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H2 text

Recursive Language Models (RLMs) are a type of language model that can generate text that is self-referential. Recursive Language Models (RLMs) are a type of language model that can generate text that is self-referential. Recursive Language Models (RLMs) are a type of language model that can generate text that is self-referential. Recursive Language Models (RLMs) are a type of language model that can generate text that is self-referential.

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AlexNet is a convolutional neural network that was used to win the ImageNet Large Scale Visual Recognition Challenge in 2012.

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Code block example

import torch
import torch.nn as nn
import torch.optim as optim

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0)
        self.conv2 = nn.Conv2d(96, 256, kernel_size=5, stride=1, padding=2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        return x

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Math block example

\begin{aligned} \mathcal{J}_\mathrm{GRPO}(\theta) &= \mathbb{E}_{q \sim P(Q),\ \{o_i\}_{i=1}^{G} \sim \pi_{\theta_\mathrm{old}}(\cdot \mid q)} \left[ \frac{1}{G}\sum_{i=1}^G \frac{1}{|o_i|}\sum_{t=1}^{|o_i|} \left( \min\left( \rho_{i,t}\hat{A}_{i,t}, \operatorname{clip}(\rho_{i,t}, 1-\epsilon, 1+\epsilon)\hat{A}_{i,t} \right) - \beta D_{i,t}^{\mathrm{KL}} \right) \right], \\ \rho_{i,t} &= \frac{\pi_\theta(o_{i,t} \mid q,o_{i,<t})} {\pi_{\theta_\mathrm{old}}(o_{i,t} \mid q,o_{i,<t})}, \qquad \hat{A}_{i,t} = \frac{r_i - \operatorname{mean}(r_1,\ldots,r_G)} {\operatorname{std}(r_1,\ldots,r_G)}, \\ D_{i,t}^{\mathrm{KL}} &= \frac{\pi_\mathrm{ref}(o_{i,t} \mid q,o_{i,<t})} {\pi_\theta(o_{i,t} \mid q,o_{i,<t})} - \log \frac{\pi_\mathrm{ref}(o_{i,t} \mid q,o_{i,<t})} {\pi_\theta(o_{i,t} \mid q,o_{i,<t})} - 1 \end{aligned}