METTA preprint combines within-episode memory and cross-episode summaries for reinforcement-learning agents

METTA combines within-episode neural memory with compact summaries across episodes, according to its preprint abstract. The authors report adaptation results on held-out reinforcement-learning tasks. This is research on meta-RL agents, rather than evidence that a general-purpose language-model assistant has acquired reliable long-term memory.