Latent Memory Palace

Latent Memory Palace: Variational inference framework enables adaptive reasoning for continuous control policies

Researchers introduce Latent Memory Palace (LMP), a method that formulates reasoning for continuous control policies as variational inference over an autoregressive latent distribution, analogous to a memory palace. The approach derives a latent-space reinforcement learning technique to optimize the variational lower bound, yielding a policy (LMP-π) with adaptive test-time compute allocation and a variable-length action tokenizer (LMP-tok) that improves downstream autoregressive policies. The work addresses the gap between language model chain-of-thought reasoning and continuous control, where language-space reasoning lacks spatial granularity. Results are demonstrated in both simulation and real-world domains.