Bypassing autoregressive token overhead significantly speeds up LLM inference.
Reasoning in Memory (RiM) enables latent reasoning without the need to "think out loud." By reasoning directly within a dedicated latent workspace—working memory—no overhead of generating explicit reasoning tokens. Dramatically faster inference with the same quality of reasoning.
Do we think this will make the shape rotators happy because reasoning will be done without an inner monologue?
Reasoning in Memory (RiM) enables latent reasoning without the need to "think out loud." By reasoning directly within a dedicated latent workspace—working memory—no overhead of generating explicit reasoning tokens. Dramatically faster inference with the same quality of reasoning.
Bypassing autoregressive token overhead significantly speeds up LLM inference.
Reasoning in Memory (RiM) enables latent reasoning without the need to "think out loud." By reasoning directly within a dedicated latent workspace—working memory—no overhead of generating explicit reasoning tokens. Dramatically faster inference with the same quality of reasoning.
Users are excited about the RiM technique for faster latent reasoning in LLMs because of strong admiration for the researchers and interest in seeing NXAI develop a full language model.