Hidden Decoding at Scale proposes latent computation scaling for LLMs

The paper 'Hidden Decoding at Scale' investigates scaling LLM performance by increasing computation at inference time rather than pretraining larger models. Experiments demonstrate that an existing backbone can continue to improve with more compute allocated during decoding.