Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach https://arxiv.org/abs/2502.05171 https://twitter.com/akazlev/status/1890193777526292745
We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters. New research paper shows how LLMs can "think" internally before outputting a single token!Unlike Chain of Thought, this "latent reasoning" happens in the model's hidden space.TONS of benefits from this approach.Let me break down this fascinating paper... -- You received this message because you are subscribed to the Google Groups "Everything List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/everything-list/1531522108.13183.1739493152683%40mail.yahoo.com.
