{"id":"arxiv:2405.15071","title":"Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization","date":"2024-05-23","section":"Reasoning and the \"physics\" of language models","quality":0.461893,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/","source_url":"https://arxiv.org/abs/2405.15071","text_available":true,"authors":["Boshi Wang","Xiang Yue","Yu Su"],"outgoing":[{"source":"arxiv:2405.15071","target":"arxiv:1807.03819","evidence":"title","id":"arxiv:1807.03819","title":"Universal Transformers","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1d843516d980c3404cb622a2a490edb535a3afe27a85a980335a54b5fcd2220a/"},{"source":"arxiv:2405.15071","target":"arxiv:2301.05217","evidence":"title","id":"arxiv:2301.05217","title":"Progress measures for grokking via mechanistic 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Generalization","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-1.html#text"},{"title":"Abstract","offset":100,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-1.html#text"},{"title":"1 Introduction","offset":1692,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-1.html#text"},{"title":"2 General Setup","offset":5964,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-1.html#text"},{"title":"3 Composition—Delayed Generalization without 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Circuit","offset":23795,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-2.html#text"},{"title":"5 The Power of Parametric Memory for Complex Reasoning","offset":29289,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-2.html#text"},{"title":"6 Related Work","offset":34717,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"7 Conclusion","offset":38494,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Limitations","offset":39259,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Acknowledgement","offset":40687,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Appendix A Training Details","offset":41284,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Appendix B Effect of Model Scale","offset":41501,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Appendix C Effect of Tokenizations","offset":41975,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Appendix D More Details on Circuit Analysis","offset":44638,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"D.1 Composition","offset":44686,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"D.2 Comparison","offset":45690,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"Appendix E Additional Results","offset":46346,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"E.1 Weight decay","offset":46380,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"E.2 Transformer with parameter sharing","offset":46644,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/part-3.html#text"},{"title":"E.3 Comparison task across different inferred/atomic 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