pith. sign in

Thus, the expected loss isLϵ ∞ =L 0 −(1−ϵ)∆−ϵ∆ ′

Phase from1 ≤k≤k i: all tasks are learned, with expressible tasks obtaining loss L0 − ∆and inexpressible tasks obtaining lossL0 −∆ ′

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Olmo Hybrid: From Theory to Practice and Back

cs.LG · 2026-04-03 · conditional · novelty 6.0

A 7B hybrid attention-recurrent model outperforms its pure-transformer counterpart on pretraining metrics and scales more efficiently, supported by a proof that hybrids are strictly more expressive than either transformers or linear RNNs.

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  • Olmo Hybrid: From Theory to Practice and Back cs.LG · 2026-04-03 · conditional · none · ref 12

    A 7B hybrid attention-recurrent model outperforms its pure-transformer counterpart on pretraining metrics and scales more efficiently, supported by a proof that hybrids are strictly more expressive than either transformers or linear RNNs.