Latent Space Reasoning
Latent Space reasoning is at least 10X better than word space reasoning
Paper: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Researchers from are interested in evaluating a novel language model architecture that enhances reasoning by scaling test-time computation within a latent space.
Hmm..What’s the background?
Traditional models increase computation by generating more tokens or using chain-of-thought, this model iteratively refines its "thinking" in a continuous, hidden space without specialized training data. We use complex, recurrent patterns in the brain for thought before verbalizing answers.
So what is proposed in the research paper?
Here are the main insights:
The architecture supports per-token adaptive compute, (self)-speculative decoding, and KV-cache sharing at inference time.
The model can be run with fewer iterations to draft the next N tokens in the generated sequence, which can then be verified with any desired number of iterations M > N later
The model exhibits context-dependent behaviors in latent space, such as "orbiting" when responding to prompts requiring numerical reasoning
A 3.5 billion parameter model was trained on 800 billion tokens, demonstrating that the recurrent approach can compete with larger models and offering advantages like adaptive computation and KV-cache sharing.
What’s next?
Future work could explore the relationship between recurrent depth and other modern architecture improvements, like efficient sequence mixing operations. The model could incorporate multiple successive recurrent stages.
Latent Space reasoning is at least 10X better than word space reasoning
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