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Here we present a high-performance online learning algorithm that merely doubles the memory and computational requirements of a single inference pass. We achieve this by leveraging independent recurrent modules in multi-layer networks, an architectural motif that has recently been shown to be particularly powerful. Experiments on synthetic memory problems and on the challenging long-range arena benchmark suite reveal that our algorithm performs competitively, establishing a new standard for what can be achieved through online learning. This ability to learn long-range dependencies offers a new perspective on learning in the brain and opens a promising avenue in neuromorphic computing.","url_abs":"https://arxiv.org/abs/2305.15947v2","url_pdf":"https://arxiv.org/pdf/2305.15947v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"online-learning-of-long-range-dependencies","repo_url":"https://github.com/nicolaszucchet/online-learning-lr-dependencies","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null},{"paper_slug":"online-learning-of-long-range-dependencies","repo_url":"https://github.com/nicolaszucchet/minimal-lru","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2305.15947","atlas_url":"https://app.syntology.ai/?focus=2305.15947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15947"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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