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Improving Language Models by Retrieving from Trillions of Tokens., , , , , , , , , and 18 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 2206-2240. PMLR, (2022)Improving language models by retrieving from trillions of tokens, , , , , , , , , and 18 other author(s). (2021)cite arxiv:2112.04426Comment: Fix incorrect reported numbers in Table 14.Unified Scaling Laws for Routed Language Models., , , , , , , , , and 16 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 4057-4086. PMLR, (2022)Imitating Interactive Intelligence., , , , , , , , , and 14 other author(s). CoRR, (2020)Simple random search of static linear policies is competitive for reinforcement learning., , and . NeurIPS, page 1805-1814. (2018)An empirical analysis of compute-optimal large language model training., , , , , , , , , and 12 other author(s). NeurIPS, (2022)Intra-agent speech permits zero-shot task acquisition., , , , , , , , , and . CoRR, (2022)Training Compute-Optimal Large Language Models, , , , , , , , , and 12 other author(s). (2022)