I came across an interesting paper today: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. It explores a novel language model architecture that can scale computation at test time by iterating a recurrent block, rather than just generating more tokens. What fascinates me is the efficiency of it all—they hypothesize that a model using this strategy can achieve performance equivalent to much larger models while using fewer parameters, making it feasible for smaller hardware. Philosophically, it almost feels like the model is actually thinking rather than just regurgitating tokens. I haven’t fully wrapped my head around all the details yet, but I find the idea incredibly compelling. Looking forward to diving deeper into it this weekend!