CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield
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Computer Science > Computation and Language
arXiv:2606.31796 (cs)
[Submitted on 30 Jun 2026]
Title:CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield
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Abstract:We study three complementary techniques for training compute-efficient language models.
(1) Selective supervision and per-token efficiency. Selective Ground Truth Token Training (SGT) concentrates supervision on the ~15% of output tokens that carry semantic payload. Through positive gradient coupling in position-shared transformer weights -- a token-level instance of auxiliary-task transfer -- the remaining 85% of unsupervised tokens still improve substantially, giving a 4.5x per-supervised-token efficiency (at the step-100 eval optimum, ~67% of the full-sequence loss reduction is recovered from 15% of the supervision). We prove that this improvement on unsupervised tokens is guaranteed whenever the gradient coupling coefficient gamma-bar = 0.72 is positive (Theorem 1), and show the effect is a property of natural-language structure: it collapses on shuffled text.
(2) Depth compression with recurrent recovery. A 48-layer, 1B-parameter transformer is compressed to 6 layers (227M) by averaging adjacent layers and restored through learned recurrent unrolling. With 34 effective recurrent layers it reaches a held-out loss of 2.934, within measurement noise of a 566M dense model at 2.926 -- a 2.5x reduction in parameters.
(3) Fusion of compressed experts. Assembling several compressed models as a Mixture of Efficient Experts (MoEE) with multi-token prediction improves over each single expert at comparable active parameters: a 2-expert MoEE reaches loss 2.789 versus 2.926 for the best single compressed model.
We validate these techniques on CHERRY-1.8B, a Korean foundation model whose every trainable parameter derives from our own training runs. We are explicit throughout about the scope of the evidence (one model family, Korean data, loss-based metrics) and about which claims are established versus prospective.
| Comments: | 33 pages, 3 figures, 28 tables. Preprint. Figures are native TikZ/pgfplots. Evaluation is loss-based; downstream benchmarks (KMMLU, HAERAE, KoBEST, MMLU) and selection-control ablations (random-15%, top-loss-15%) to appear in a future version |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.31796 [cs.CL] |
| (or arXiv:2606.31796v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31796
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield, by Dohyeon Kwon and 1 other authors
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